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ae4a0fc5-bb0d-4b73-8539-f8e0f17c7a01 | structure-determination | 2108.02706 | null | https://arxiv.org/abs/2108.02706v1 | https://arxiv.org/pdf/2108.02706v1.pdf | Structure determination | While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensional protein structures through computational analysis. We provide an overview of existing computational techniques, to validate, simulate, predict and analyse protein structures. More importantly, it will aim to provide practical knowledge about how and when to use such techniques. We will consider proteins from three major vantage points: Protein structure quantification, Protein structure prediction, and Protein simulation & dynamics. The main emphasis of this work is to provide a background on experimental techniques for protein structure determination. The focus is set on X-ray crystallography and Nuclear Magnetic Resonance spectroscopy (NMR), which are by far the main methods used to determine the structure of soluble proteins. We will also introduce cryogenic Electron Microscopy (cryo-EM) and electron diffraction which are more suited to analyze membrane proteins and larger protein complexes. At the end, more qualitative techniques are summarized that are used to obtain insight on the overall structure and dynamics of proteins. Note that this introduction to protein structure determination aims at familiarizing the reader to different experimental techniques, their benefits and bottlenecks, but that a thorough mathematical and technical description of the concept is beyond the scope of this work. | ['K. Anton Feenstra', 'Sanne Abeln', 'Katharina Waury', 'Jose Gavaldá-García', 'Hugo van Ingen', 'Bas Stringer', 'Halima Mouhib'] | 2021-08-05 | null | null | null | null | ['cryogenic-electron-microscopy-cryo-em'] | ['computer-vision'] | [ 3.50267559e-01 -1.72770277e-01 -1.81482002e-01 -1.51812598e-01
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916d311c-fecb-4d46-88ed-1d0a36080557 | slav-ner-the-3rd-cross-lingual-challenge-on | null | null | https://aclanthology.org/2021.bsnlp-1.15 | https://aclanthology.org/2021.bsnlp-1.15.pdf | Slav-NER: the 3rd Cross-lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic Languages | This paper describes Slav-NER: the 3rd Multilingual Named Entity Challenge in Slavic languages. The tasks involve recognizing mentions of named entities in Web documents, normalization of the names, and cross-lingual linking. The Challenge covers six languages and five entity types, and is organized as part of the 8th Balto-Slavic Natural Language Processing Workshop, co-located with the EACL 2021 Conference. Ten teams participated in the competition. Performance for the named entity recognition task reached 90% F-measure, much higher than reported in the first edition of the Challenge. Seven teams covered all six languages, and five teams participated in the cross-lingual entity linking task. Detailed valuation information is available on the shared task web page. | ['Roman Yangarber', 'Josef Steinberger', 'Vasyl Starko', 'Marko Robnik-Sikonja', 'Ivaylo Radev', 'Pavel Přibáň', 'Senja Pollak', 'Lidia Pivovarova', 'Petya Osenova', 'Preslav Nakov', 'Michał Marcińczuk', 'Maria Lebedeva', 'Olga Kanishcheva', 'Zara Kancheva', 'Bogdan Babych', 'Jakub Piskorski'] | null | null | null | null | eacl-bsnlp-2021-4 | ['cross-lingual-entity-linking'] | ['natural-language-processing'] | [-5.45303762e-01 7.31322393e-02 -5.04935801e-01 -4.04019207e-01
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0ef34d77-9bcd-41ed-9508-a95d46889d23 | exponential-concentration-for-mutual | null | null | http://papers.nips.cc/paper/4768-exponential-concentration-for-mutual-information-estimation-with-application-to-forests | http://papers.nips.cc/paper/4768-exponential-concentration-for-mutual-information-estimation-with-application-to-forests.pdf | Exponential Concentration for Mutual Information Estimation with Application to Forests | We prove a new exponential concentration inequality for a plug-in estimator of the Shannon mutual information. Previous results on mutual information estimation only bounded expected error. The advantage of having the exponential inequality is that, combined with the union bound, we can guarantee accurate estimators of the mutual information for many pairs of random variables simultaneously. As an application, we show how to use such a result to optimally estimate the density function and graph of a distribution which is Markov to a forest graph. | ['John D. Lafferty', 'Han Liu', 'Larry Wasserman'] | 2012-12-01 | null | null | null | neurips-2012-12 | ['mutual-information-estimation'] | ['methodology'] | [ 2.45339379e-01 3.97111326e-01 -2.40799680e-01 -2.82620519e-01
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c8d2f1f2-61f0-46ca-b461-0b587b56b17e | nestedformer-nested-modality-aware | 2208.14876 | null | https://arxiv.org/abs/2208.14876v1 | https://arxiv.org/pdf/2208.14876v1.pdf | NestedFormer: Nested Modality-Aware Transformer for Brain Tumor Segmentation | Multi-modal MR imaging is routinely used in clinical practice to diagnose and investigate brain tumors by providing rich complementary information. Previous multi-modal MRI segmentation methods usually perform modal fusion by concatenating multi-modal MRIs at an early/middle stage of the network, which hardly explores non-linear dependencies between modalities. In this work, we propose a novel Nested Modality-Aware Transformer (NestedFormer) to explicitly explore the intra-modality and inter-modality relationships of multi-modal MRIs for brain tumor segmentation. Built on the transformer-based multi-encoder and single-decoder structure, we perform nested multi-modal fusion for high-level representations of different modalities and apply modality-sensitive gating (MSG) at lower scales for more effective skip connections. Specifically, the multi-modal fusion is conducted in our proposed Nested Modality-aware Feature Aggregation (NMaFA) module, which enhances long-term dependencies within individual modalities via a tri-orientated spatial-attention transformer, and further complements key contextual information among modalities via a cross-modality attention transformer. Extensive experiments on BraTS2020 benchmark and a private meningiomas segmentation (MeniSeg) dataset show that the NestedFormer clearly outperforms the state-of-the-arts. The code is available at https://github.com/920232796/NestedFormer. | ['Lei Zhu', 'Tong Han', 'Liang Wan', 'Lequan Yu', 'Zhaohu Xing'] | 2022-08-31 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 3.31582397e-01 2.20716447e-02 -1.31204173e-01 -5.04000783e-01
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c4610a86-41db-4d8c-ad30-36fb362e2d33 | cbnetv2-a-composite-backbone-network | 2107.00420 | null | https://arxiv.org/abs/2107.00420v7 | https://arxiv.org/pdf/2107.00420v7.pdf | CBNet: A Composite Backbone Network Architecture for Object Detection | Modern top-performing object detectors depend heavily on backbone networks, whose advances bring consistent performance gains through exploring more effective network structures. In this paper, we propose a novel and flexible backbone framework, namely CBNetV2, to construct high-performance detectors using existing open-sourced pre-trained backbones under the pre-training fine-tuning paradigm. In particular, CBNetV2 architecture groups multiple identical backbones, which are connected through composite connections. Specifically, it integrates the high- and low-level features of multiple backbone networks and gradually expands the receptive field to more efficiently perform object detection. We also propose a better training strategy with assistant supervision for CBNet-based detectors. Without additional pre-training of the composite backbone, CBNetV2 can be adapted to various backbones (CNN-based vs. Transformer-based) and head designs of most mainstream detectors (one-stage vs. two-stage, anchor-based vs. anchor-free-based). Experiments provide strong evidence that, compared with simply increasing the depth and width of the network, CBNetV2 introduces a more efficient, effective, and resource-friendly way to build high-performance backbone networks. Particularly, our Dual-Swin-L achieves 59.4% box AP and 51.6% mask AP on COCO test-dev under the single-model and single-scale testing protocol, which is significantly better than the state-of-the-art result (57.7% box AP and 50.2% mask AP) achieved by Swin-L, while the training schedule is reduced by 6$\times$. With multi-scale testing, we push the current best single model result to a new record of 60.1% box AP and 52.3% mask AP without using extra training data. Code is available at https://github.com/VDIGPKU/CBNetV2. | ['Haibin Ling', 'Jingdong Chen', 'Wei Chu', 'Zhi Tang', 'Yongtao Wang', 'Yudong Liu', 'Xiaojie Chu', 'TingTing Liang'] | 2021-07-01 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-1.42396510e-01 1.25173837e-01 -3.18554133e-01 -2.70509690e-01
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a84b1e1a-f1dc-4802-8f31-8b2232f0ace3 | bias-reduced-multi-step-hindsight-experience | 2102.12962 | null | https://arxiv.org/abs/2102.12962v3 | https://arxiv.org/pdf/2102.12962v3.pdf | Bias-reduced Multi-step Hindsight Experience Replay for Efficient Multi-goal Reinforcement Learning | Multi-goal reinforcement learning is widely applied in planning and robot manipulation. Two main challenges in multi-goal reinforcement learning are sparse rewards and sample inefficiency. Hindsight Experience Replay (HER) aims to tackle the two challenges via goal relabeling. However, HER-related works still need millions of samples and a huge computation. In this paper, we propose Multi-step Hindsight Experience Replay (MHER), incorporating multi-step relabeled returns based on $n$-step relabeling to improve sample efficiency. Despite the advantages of $n$-step relabeling, we theoretically and experimentally prove the off-policy $n$-step bias introduced by $n$-step relabeling may lead to poor performance in many environments. To address the above issue, two bias-reduced MHER algorithms, MHER($\lambda$) and Model-based MHER (MMHER) are presented. MHER($\lambda$) exploits the $\lambda$ return while MMHER benefits from model-based value expansions. Experimental results on numerous multi-goal robotic tasks show that our solutions can successfully alleviate off-policy $n$-step bias and achieve significantly higher sample efficiency than HER and Curriculum-guided HER with little additional computation beyond HER. | ['Jiangpeng Yan', 'Xiu Li', 'Lanqing Li', 'Dijun Luo', 'Feng Luo', 'Yu Yang', 'Jiafei Lyu', 'Rui Yang'] | 2021-02-25 | null | null | null | null | ['multi-goal-reinforcement-learning'] | ['methodology'] | [-1.03258535e-01 1.59111589e-01 -4.32868183e-01 -2.06559435e-01
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e32d38a8-0d40-4293-a43a-a9d70bbd8931 | from-temporal-to-contemporaneous-iterative | 2306.00624 | null | https://arxiv.org/abs/2306.00624v1 | https://arxiv.org/pdf/2306.00624v1.pdf | From Temporal to Contemporaneous Iterative Causal Discovery in the Presence of Latent Confounders | We present a constraint-based algorithm for learning causal structures from observational time-series data, in the presence of latent confounders. We assume a discrete-time, stationary structural vector autoregressive process, with both temporal and contemporaneous causal relations. One may ask if temporal and contemporaneous relations should be treated differently. The presented algorithm gradually refines a causal graph by learning long-term temporal relations before short-term ones, where contemporaneous relations are learned last. This ordering of causal relations to be learnt leads to a reduction in the required number of statistical tests. We validate this reduction empirically and demonstrate that it leads to higher accuracy for synthetic data and more plausible causal graphs for real-world data compared to state-of-the-art algorithms. | ['Gal Novik', 'Yaniv Gurwicz', 'Shami Nisimov', 'Raanan Y. Rohekar'] | 2023-06-01 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 4.44250613e-01 2.55907893e-01 -4.28309351e-01 -3.83571863e-01
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3ac611d0-a870-43a9-a881-f892f6eeca68 | adaptive-wing-loss-for-robust-face-alignment | 1904.07399 | null | https://arxiv.org/abs/1904.07399v3 | https://arxiv.org/pdf/1904.07399v3.pdf | Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression | Heatmap regression with a deep network has become one of the mainstream approaches to localize facial landmarks. However, the loss function for heatmap regression is rarely studied. In this paper, we analyze the ideal loss function properties for heatmap regression in face alignment problems. Then we propose a novel loss function, named Adaptive Wing loss, that is able to adapt its shape to different types of ground truth heatmap pixels. This adaptability penalizes loss more on foreground pixels while less on background pixels. To address the imbalance between foreground and background pixels, we also propose Weighted Loss Map, which assigns high weights on foreground and difficult background pixels to help training process focus more on pixels that are crucial to landmark localization. To further improve face alignment accuracy, we introduce boundary prediction and CoordConv with boundary coordinates. Extensive experiments on different benchmarks, including COFW, 300W and WFLW, show our approach outperforms the state-of-the-art by a significant margin on various evaluation metrics. Besides, the Adaptive Wing loss also helps other heatmap regression tasks. Code will be made publicly available at https://github.com/protossw512/AdaptiveWingLoss. | ['Li Fuxin', 'Xinyao Wang', 'Liefeng Bo'] | 2019-04-16 | adaptive-wing-loss-for-robust-face-alignment-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Adaptive_Wing_Loss_for_Robust_Face_Alignment_via_Heatmap_Regression_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Adaptive_Wing_Loss_for_Robust_Face_Alignment_via_Heatmap_Regression_ICCV_2019_paper.pdf | iccv-2019-10 | ['robust-face-alignment'] | ['computer-vision'] | [-2.28794396e-01 1.04190670e-01 -3.71946067e-01 -7.36093462e-01
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1ebb27bb-5022-4eee-89db-cc43d64fd6b3 | benchmark-for-anonymous-video-analytics | 2009.14684 | null | https://arxiv.org/abs/2009.14684v3 | https://arxiv.org/pdf/2009.14684v3.pdf | Benchmark for Anonymous Video Analytics | Out-of-home audience measurement aims to count and characterize the people exposed to advertising content in the physical world. While audience measurement solutions based on computer vision are of increasing interest, no commonly accepted benchmark exists to evaluate and compare their performance. In this paper, we propose the first benchmark for digital out-of-home audience measurement that evaluates the vision-based tasks of audience localization and counting, and audience demographics. The benchmark is composed of a novel, dataset captured at multiple locations and a set of performance measures. Using the benchmark, we present an in-depth comparison of eight open-source algorithms on four hardware platforms with GPU and CPU-optimized inferences and of two commercial off-the-shelf solutions for localization, count, age, and gender estimation. This benchmark and related open-source codes are available at http://ava.eecs.qmul.ac.uk. | ['Ricardo Sanchez-Matilla', 'Andrea Cavallaro'] | 2020-09-30 | null | null | null | null | ['age-and-gender-estimation'] | ['computer-vision'] | [-1.39001936e-01 -4.66006905e-01 -1.45769492e-01 -4.27335769e-01
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bf11b663-27e7-4112-adec-ad898f0f003f | the-comma-dataset-v0-2-annotating-aggression | 2111.10390 | null | https://arxiv.org/abs/2111.10390v1 | https://arxiv.org/pdf/2111.10390v1.pdf | The ComMA Dataset V0.2: Annotating Aggression and Bias in Multilingual Social Media Discourse | In this paper, we discuss the development of a multilingual dataset annotated with a hierarchical, fine-grained tagset marking different types of aggression and the "context" in which they occur. The context, here, is defined by the conversational thread in which a specific comment occurs and also the "type" of discursive role that the comment is performing with respect to the previous comment. The initial dataset, being discussed here (and made available as part of the ComMA@ICON shared task), consists of a total 15,000 annotated comments in four languages - Meitei, Bangla, Hindi, and Indian English - collected from various social media platforms such as YouTube, Facebook, Twitter and Telegram. As is usual on social media websites, a large number of these comments are multilingual, mostly code-mixed with English. The paper gives a detailed description of the tagset being used for annotation and also the process of developing a multi-label, fine-grained tagset that can be used for marking comments with aggression and bias of various kinds including gender bias, religious intolerance (called communal bias in the tagset), class/caste bias and ethnic/racial bias. We also define and discuss the tags that have been used for marking different the discursive role being performed through the comments, such as attack, defend, etc. We also present a statistical analysis of the dataset as well as results of our baseline experiments with developing an automatic aggression identification system using the dataset developed. | ['Yogesh Dawer', 'Akash Bhagat', 'Siddharth Singh', 'Shyam Ratan', 'Laishram Niranjana Devi', 'Enakshi Nandi', 'Ritesh Kumar'] | 2021-11-19 | null | https://aclanthology.org/2022.lrec-1.441 | https://aclanthology.org/2022.lrec-1.441.pdf | lrec-2022-6 | ['aggression-identification'] | ['natural-language-processing'] | [-5.82605600e-01 -4.12632748e-02 5.60624488e-02 -4.48797494e-01
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f335caec-2b20-47e5-85e5-d770a2a8ec7a | adversarial-robustness-with-non-uniform | 2102.12002 | null | https://arxiv.org/abs/2102.12002v4 | https://arxiv.org/pdf/2102.12002v4.pdf | Adversarial Robustness with Non-uniform Perturbations | Robustness of machine learning models is critical for security related applications, where real-world adversaries are uniquely focused on evading neural network based detectors. Prior work mainly focus on crafting adversarial examples (AEs) with small uniform norm-bounded perturbations across features to maintain the requirement of imperceptibility. However, uniform perturbations do not result in realistic AEs in domains such as malware, finance, and social networks. For these types of applications, features typically have some semantically meaningful dependencies. The key idea of our proposed approach is to enable non-uniform perturbations that can adequately represent these feature dependencies during adversarial training. We propose using characteristics of the empirical data distribution, both on correlations between the features and the importance of the features themselves. Using experimental datasets for malware classification, credit risk prediction, and spam detection, we show that our approach is more robust to real-world attacks. Finally, we present robustness certification utilizing non-uniform perturbation bounds, and show that non-uniform bounds achieve better certification. | ['Sergul Aydore', 'Luca Melis', 'Jeffrey Bickford', 'Ecenaz Erdemir'] | 2021-02-24 | null | http://proceedings.neurips.cc/paper/2021/hash/9fd98f856d3ca2086168f264a117ed7c-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/9fd98f856d3ca2086168f264a117ed7c-Paper.pdf | neurips-2021-12 | ['spam-detection'] | ['natural-language-processing'] | [ 3.50202113e-01 -1.21867009e-01 -9.41936597e-02 -1.22261502e-01
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8a644c08-018a-484a-9b90-73fdec5fa74a | equivalent-classification-mapping-for-weakly | 2008.07728 | null | https://arxiv.org/abs/2008.07728v2 | https://arxiv.org/pdf/2008.07728v2.pdf | Equivalent Classification Mapping for Weakly Supervised Temporal Action Localization | Weakly supervised temporal action localization is a newly emerging yet widely studied topic in recent years. The existing methods can be categorized into two localization-by-classification pipelines, i.e., the pre-classification pipeline and the post-classification pipeline. The pre-classification pipeline first performs classification on each video snippet and then aggregate the snippet-level classification scores to obtain the video-level classification score. In contrast, the post-classification pipeline aggregates the snippet-level features first and then predicts the video-level classification score based on the aggregated feature. Although the classifiers in these two pipelines are used in different ways, the role they play is exactly the same---to classify the given features to identify the corresponding action categories. To this end, an ideal classifier can make both pipelines work. This inspires us to simultaneously learn these two pipelines in a unified framework to obtain an effective classifier. Specifically, in the proposed learning framework, we implement two parallel network streams to model the two localization-by-classification pipelines simultaneously and make the two network streams share the same classifier. This achieves the novel Equivalent Classification Mapping (ECM) mechanism. Moreover, we discover that an ideal classifier may possess two characteristics: 1) The frame-level classification scores obtained from the pre-classification stream and the feature aggregation weights in the post-classification stream should be consistent; 2) The classification results of these two streams should be identical. Based on these two characteristics, we further introduce a weight-transition module and an equivalent training strategy into the proposed learning framework, which assists to thoroughly mine the equivalence mechanism. | ['Dingwen Zhang', 'Le Yang', 'Junwei Han', 'Tao Zhao'] | 2020-08-18 | null | null | null | null | ['weakly-supervised-temporal-action'] | ['computer-vision'] | [ 3.21295500e-01 -4.13686991e-01 -5.18261015e-01 -4.85878557e-01
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32e5dea5-4a35-4b51-bc4f-63e612a6c033 | joint-learning-of-salient-object-detection | 2203.04895 | null | https://arxiv.org/abs/2203.04895v2 | https://arxiv.org/pdf/2203.04895v2.pdf | Joint Learning of Salient Object Detection, Depth Estimation and Contour Extraction | Benefiting from color independence, illumination invariance and location discrimination attributed by the depth map, it can provide important supplemental information for extracting salient objects in complex environments. However, high-quality depth sensors are expensive and can not be widely applied. While general depth sensors produce the noisy and sparse depth information, which brings the depth-based networks with irreversible interference. In this paper, we propose a novel multi-task and multi-modal filtered transformer (MMFT) network for RGB-D salient object detection (SOD). Specifically, we unify three complementary tasks: depth estimation, salient object detection and contour estimation. The multi-task mechanism promotes the model to learn the task-aware features from the auxiliary tasks. In this way, the depth information can be completed and purified. Moreover, we introduce a multi-modal filtered transformer (MFT) module, which equips with three modality-specific filters to generate the transformer-enhanced feature for each modality. The proposed model works in a depth-free style during the testing phase. Experiments show that it not only significantly surpasses the depth-based RGB-D SOD methods on multiple datasets, but also precisely predicts a high-quality depth map and salient contour at the same time. And, the resulted depth map can help existing RGB-D SOD methods obtain significant performance gain. The source code will be publicly available at https://github.com/Xiaoqi-Zhao-DLUT/MMFT. | ['Huchuan Lu', 'Lihe Zhang', 'Youwei Pang', 'Xiaoqi Zhao'] | 2022-03-09 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 1.04817376e-01 -1.80394471e-01 -2.35347703e-01 -3.96697909e-01
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cad799da-8b90-4956-9030-92f47c8cf9be | double-pessimism-is-provably-efficient-for | 2305.09659 | null | https://arxiv.org/abs/2305.09659v1 | https://arxiv.org/pdf/2305.09659v1.pdf | Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage | We study distributionally robust offline reinforcement learning (robust offline RL), which seeks to find an optimal robust policy purely from an offline dataset that can perform well in perturbed environments. We propose a generic algorithm framework \underline{D}oubly \underline{P}essimistic \underline{M}odel-based \underline{P}olicy \underline{O}ptimization ($\texttt{P}^2\texttt{MPO}$) for robust offline RL, which features a novel combination of a flexible model estimation subroutine and a doubly pessimistic policy optimization step. The \emph{double pessimism} principle is crucial to overcome the distributional shift incurred by i) the mismatch between behavior policy and the family of target policies; and ii) the perturbation of the nominal model. Under certain accuracy assumptions on the model estimation subroutine, we show that $\texttt{P}^2\texttt{MPO}$ is provably efficient with \emph{robust partial coverage data}, which means that the offline dataset has good coverage of the distributions induced by the optimal robust policy and perturbed models around the nominal model. By tailoring specific model estimation subroutines for concrete examples including tabular Robust Markov Decision Process (RMDP), factored RMDP, and RMDP with kernel and neural function approximations, we show that $\texttt{P}^2\texttt{MPO}$ enjoys a $\tilde{\mathcal{O}}(n^{-1/2})$ convergence rate, where $n$ is the number of trajectories in the offline dataset. Notably, these models, except for the tabular case, are first identified and proven tractable by this paper. To the best of our knowledge, we first propose a general learning principle -- double pessimism -- for robust offline RL and show that it is provably efficient in the context of general function approximations. | ['Han Zhong', 'Tong Zhang', 'Miao Lu', 'Jose Blanchet'] | 2023-05-16 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-2.88934261e-02 3.85673016e-01 -5.38427591e-01 1.38020784e-01
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426e4c3f-e667-4b0d-979a-560606e5dd3f | generative-mask-pyramid-network-forctcbct | 1907.00294 | null | https://arxiv.org/abs/1907.00294v4 | https://arxiv.org/pdf/1907.00294v4.pdf | Generative Mask Pyramid Network for CT/CBCT Metal Artifact Reduction with Joint Projection-Sinogram Correction | A conventional approach to computed tomography (CT) or cone beam CT (CBCT) metal artifact reduction is to replace the X-ray projection data within the metal trace with synthesized data. However, existing projection or sinogram completion methods cannot always produce anatomically consistent information to fill the metal trace, and thus, when the metallic implant is large, significant secondary artifacts are often introduced. In this work, we propose to replace metal artifact affected regions with anatomically consistent content through joint projection-sinogram correction as well as adversarial learning. To handle the metallic implants of diverse shapes and large sizes, we also propose a novel mask pyramid network that enforces the mask information across the network's encoding layers and a mask fusion loss that reduces early saturation of adversarial training. Our experimental results show that the proposed projection-sinogram correction designs are effective and our method recovers information from the metal traces better than the state-of-the-art methods. | ['S. Kevin Zhou', 'Wei-An Lin', 'Zhimin Huo', 'Jiebo Luo', 'William J. Sehnert', 'Levon Vogelsang', 'Haofu Liao'] | 2019-06-29 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 5.39805889e-01 1.29307047e-01 2.25621909e-01 -1.98285654e-01
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d7aa411d-c43c-4666-9d55-9c56db67fa5b | smiss-a-protein-function-prediction-server-by | 1607.01384 | null | http://arxiv.org/abs/1607.01384v1 | http://arxiv.org/pdf/1607.01384v1.pdf | SMISS: A protein function prediction server by integrating multiple sources | SMISS is a novel web server for protein function prediction. Three different
predictors can be selected for different usage. It integrates different sources
to improve the protein function prediction accuracy, including the query
protein sequence, protein-protein interaction network, gene-gene interaction
network, and the rules mined from protein function associations. SMISS
automatically switch to ab initio protein function prediction based on the
query sequence when there is no homologs in the database. It takes fasta format
sequences as input, and several sequences can submit together without
influencing the computation speed too much. PHP and Perl are two primary
programming language used in the server. The CodeIgniter MVC PHP web framework
and Bootstrap front-end framework are used for building the server. It can be
used in different platforms in standard web browser, such as Windows, Mac OS X,
Linux, and iOS. No plugins are needed for our website. Availability:
http://tulip.rnet.missouri.edu/profunc/. | [] | 2016-03-22 | null | null | null | null | ['protein-function-prediction'] | ['medical'] | [-1.98630039e-02 -3.01891714e-01 -3.33951533e-01 -4.68014240e-01
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a057bf8f-6a3f-4191-9c93-d8cd5fd2981e | representation-and-bias-in-multilingual-nlp | null | null | https://openreview.net/forum?id=dKwmCtp6YI | https://openreview.net/pdf?id=dKwmCtp6YI | Representation and Bias in Multilingual NLP: Insights from Controlled Experiments on Conditional Language Modeling | Inspired by the phenomenon of performance disparity between languages in machine translation, we investigate whether and to what extent languages are equally hard to "conditional-language-model". Our goal is to improve our understanding and expectation of the relationship between language, data representation, size, and performance in one-to-one conditional language modeling through a series of systematically controlled experiments with the Transformer and parallel data on the 6 diverse, official languages of the United Nations --- in 30 directions, 5 sizes, and 3 primary representation types in character, byte, and word, along with 5 alternate variants for a secondary set of controls. We observe indications suggesting a script bias on the character level, a length bias on the byte level, and a word bias that gives rise to a hierarchy in performance across languages. We also identify two types of sample-wise non-monotonicity --- while word-based representations are prone to exhibit Double Descent, length can induce unstable performance across the size range studied in a novel meta phenomenon which we term "erraticity". By eliminating statistically significant performance disparity on the character and byte levels, we show that, in the context of computing with the Transformer, there is no complexity intrinsic to languages other than that related to their statistical attributes and that performance disparity is not a necessary condition but a byproduct of word segmentation. Our application of statistical comparisons as a fairness measure also serves as a novel rigorous method for the intrinsic evaluation of languages, resolving a decades-long debate on language complexity. We hope our work helps open up new directions in the area of language and computing that would be fairer and more flexible. | ['Ada Wan'] | 2021-01-01 | null | null | null | null | ['multilingual-nlp'] | ['natural-language-processing'] | [ 1.59418210e-01 -3.01675737e-01 -4.63856965e-01 -3.47900480e-01
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35c98d5e-d0ec-459c-b93d-0fda660fab96 | end-to-end-illuminant-estimation-based-on | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Xu_End-to-End_Illuminant_Estimation_Based_on_Deep_Metric_Learning_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_End-to-End_Illuminant_Estimation_Based_on_Deep_Metric_Learning_CVPR_2020_paper.pdf | End-to-End Illuminant Estimation Based on Deep Metric Learning | Previous deep learning approaches to color constancy usually directly estimate illuminant value from input image. Such approaches might suffer heavily from being sensitive to the variation of image content. To overcome this problem, we introduce a deep metric learning approach named Illuminant-Guided Triplet Network (IGTN) to color constancy. IGTN generates an Illuminant Consistent and Discriminative Feature (ICDF) for achieving robust and accurate illuminant color estimation. ICDF is composed of semantic and color features based on a learnable color histogram scheme. In the ICDF space, regardless of the similarities of their contents, images taken under the same or similar illuminants are placed close to each other and at the same time images taken under different illuminants are placed far apart. We also adopt an end-to-end training strategy to simultaneously group image features and estimate illuminant value, and thus our approach does not have to classify illuminant in a separate module. We evaluate our method on two public datasets and demonstrate our method outperforms state-of-the-art approaches. Furthermore, we demonstrate that our method is less sensitive to image appearances, and can achieve more robust and consistent results than other methods on a High Dynamic Range dataset.
| [' Guoping Qiu', ' Bozhi Liu', ' Xianxu Hou', ' Jingxin Liu', 'Bolei Xu'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['color-constancy'] | ['computer-vision'] | [ 7.72272423e-02 -9.03211832e-01 1.35481313e-01 -6.22165859e-01
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4e9e67b8-842f-40dd-9622-5b584ff92f6a | neural-guided-ransac-learning-where-to-sample | 1905.04132 | null | https://arxiv.org/abs/1905.04132v2 | https://arxiv.org/pdf/1905.04132v2.pdf | Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses | We present Neural-Guided RANSAC (NG-RANSAC), an extension to the classic RANSAC algorithm from robust optimization. NG-RANSAC uses prior information to improve model hypothesis search, increasing the chance of finding outlier-free minimal sets. Previous works use heuristic side-information like hand-crafted descriptor distance to guide hypothesis search. In contrast, we learn hypothesis search in a principled fashion that lets us optimize an arbitrary task loss during training, leading to large improvements on classic computer vision tasks. We present two further extensions to NG-RANSAC. Firstly, using the inlier count itself as training signal allows us to train neural guidance in a self-supervised fashion. Secondly, we combine neural guidance with differentiable RANSAC to build neural networks which focus on certain parts of the input data and make the output predictions as good as possible. We evaluate NG-RANSAC on a wide array of computer vision tasks, namely estimation of epipolar geometry, horizon line estimation and camera re-localization. We achieve superior or competitive results compared to state-of-the-art robust estimators, including very recent, learned ones. | ['Carsten Rother', 'Eric Brachmann'] | 2019-05-10 | neural-guided-ransac-learning-where-to-sample-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Brachmann_Neural-Guided_RANSAC_Learning_Where_to_Sample_Model_Hypotheses_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Brachmann_Neural-Guided_RANSAC_Learning_Where_to_Sample_Model_Hypotheses_ICCV_2019_paper.pdf | iccv-2019-10 | ['camera-localization', 'horizon-line-estimation'] | ['computer-vision', 'computer-vision'] | [-5.50742820e-02 -1.44119754e-01 -2.25048602e-01 -5.90935826e-01
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76edc26f-b677-43b8-afaa-daf7b4b3f55c | online-binaural-speech-separation-of-moving | 2303.07458 | null | https://arxiv.org/abs/2303.07458v1 | https://arxiv.org/pdf/2303.07458v1.pdf | Online Binaural Speech Separation of Moving Speakers With a Wavesplit Network | Binaural speech separation in real-world scenarios often involves moving speakers. Most current speech separation methods use utterance-level permutation invariant training (u-PIT) for training. In inference time, however, the order of outputs can be inconsistent over time particularly in long-form speech separation. This situation which is referred to as the speaker swap problem is even more problematic when speakers constantly move in space and therefore poses a challenge for consistent placement of speakers in output channels. Here, we describe a real-time binaural speech separation model based on a Wavesplit network to mitigate the speaker swap problem for moving speaker separation. Our model computes a speaker embedding for each speaker at each time frame from the mixed audio, aggregates embeddings using online clustering, and uses cluster centroids as speaker profiles to track each speaker throughout the long duration. Experimental results on reverberant, long-form moving multitalker speech separation show that the proposed method is less prone to speaker swap and achieves comparable performance with u-PIT based models with ground truth tracking in both separation accuracy and preserving the interaural cues. | ['Nima Mesgarani', 'Cong Han'] | 2023-03-13 | null | null | null | null | ['online-clustering', 'speech-separation', 'speaker-separation'] | ['computer-vision', 'speech', 'speech'] | [-1.07212632e-03 -5.06970048e-01 3.57005119e-01 -2.45536596e-01
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65d8cbb0-43d6-4e13-b7a9-ac7d1fb740c9 | networked-signal-and-information-processing | 2210.13767 | null | https://arxiv.org/abs/2210.13767v2 | https://arxiv.org/pdf/2210.13767v2.pdf | Networked Signal and Information Processing | The article reviews significant advances in networked signal and information processing, which have enabled in the last 25 years extending decision making and inference, optimization, control, and learning to the increasingly ubiquitous environments of distributed agents. As these interacting agents cooperate, new collective behaviors emerge from local decisions and actions. Moreover, and significantly, theory and applications show that networked agents, through cooperation and sharing, are able to match the performance of cloud or federated solutions, while offering the potential for improved privacy, increasing resilience, and saving resources. | ['José M. F. Moura', 'Ali H. Sayed', 'Soummya Kar', 'Stefan Vlaski'] | 2022-10-25 | null | null | null | null | ['inference-optimization'] | ['audio'] | [-1.75149858e-01 -3.48225534e-02 -4.73938137e-02 -8.01219642e-02
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113a0c26-ac2b-4db6-87ab-d6242a8a150d | rvmde-radar-validated-monocular-depth | 2109.05265 | null | https://arxiv.org/abs/2109.05265v3 | https://arxiv.org/pdf/2109.05265v3.pdf | RVMDE: Radar Validated Monocular Depth Estimation for Robotics | Stereoscopy exposits a natural perception of distance in a scene, and its manifestation in 3D world understanding is an intuitive phenomenon. However, an innate rigid calibration of binocular vision sensors is crucial for accurate depth estimation. Alternatively, a monocular camera alleviates the limitation at the expense of accuracy in estimating depth, and the challenge exacerbates in harsh environmental conditions. Moreover, an optical sensor often fails to acquire vital signals in harsh environments, and radar is used instead, which gives coarse but more accurate signals. This work explores the utility of coarse signals from radar when fused with fine-grained data from a monocular camera for depth estimation in harsh environmental conditions. A variant of feature pyramid network (FPN) extensively operates on fine-grained image features at multiple scales with a fewer number of parameters. FPN feature maps are fused with sparse radar features extracted with a Convolutional neural network. The concatenated hierarchical features are used to predict the depth with ordinal regression. We performed experiments on the nuScenes dataset, and the proposed architecture stays on top in quantitative evaluations with reduced parameters and faster inference. The depth estimation results suggest that the proposed techniques can be used as an alternative to stereo depth estimation in critical applications in robotics and self-driving cars. The source code will be available in the following: \url{https://github.com/MI-Hussain/RVMDE}. | ['Moongu Jeon', 'Muhammad Aasim Rafique', 'Muhamamd Ishfaq Hussain'] | 2021-09-11 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [ 2.32231572e-01 -3.29807103e-01 3.76934595e-02 -5.44552684e-01
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f4a48471-69ef-41e6-920d-f4249c0a1f1a | neural-granular-sound-synthesis | 2008.01393 | null | https://arxiv.org/abs/2008.01393v3 | https://arxiv.org/pdf/2008.01393v3.pdf | Neural Granular Sound Synthesis | Granular sound synthesis is a popular audio generation technique based on rearranging sequences of small waveform windows. In order to control the synthesis, all grains in a given corpus are analyzed through a set of acoustic descriptors. This provides a representation reflecting some form of local similarities across the grains. However, the quality of this grain space is bound by that of the descriptors. Its traversal is not continuously invertible to signal and does not render any structured temporality. We demonstrate that generative neural networks can implement granular synthesis while alleviating most of its shortcomings. We efficiently replace its audio descriptor basis by a probabilistic latent space learned with a Variational Auto-Encoder. In this setting the learned grain space is invertible, meaning that we can continuously synthesize sound when traversing its dimensions. It also implies that original grains are not stored for synthesis. Another major advantage of our approach is to learn structured paths inside this latent space by training a higher-level temporal embedding over arranged grain sequences. The model can be applied to many types of libraries, including pitched notes or unpitched drums and environmental noises. We report experiments on the common granular synthesis processes as well as novel ones such as conditional sampling and morphing. | ['Adrien Bitton', 'Tatsuya Harada', 'Philippe Esling'] | 2020-08-04 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 3.83103609e-01 1.34353250e-01 1.86037943e-01 3.49089950e-02
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8cd1d030-7655-4efe-be25-2cc56e902af6 | the-liver-tumor-segmentation-benchmark-lits | 1901.04056 | null | https://arxiv.org/abs/1901.04056v2 | https://arxiv.org/pdf/1901.04056v2.pdf | The Liver Tumor Segmentation Benchmark (LiTS) | In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in \url{http://medicaldecathlon.com/}. In addition, both data and online evaluation are accessible via \url{www.lits-challenge.com}. | ['Bjoern Menze', 'Christopher Pal', 'Spyridon Bakas', 'Jorge Cardoso', 'Liping Zhang', 'Miao Yu', 'Yading Yuan', 'Simon Chun-Ho Yu', 'Xiaoping Yang', 'Daguang Xu', 'Jianrong Wu', 'Leon Weninger', 'Chunliang Wang', 'Christian Wachinger', 'Jordi Torres', 'Zengming Shen', 'Ignacio Sarasua', 'Oliver Rippel', 'Jordi Pont-Tuset', 'Jens Petersen', 'Mathias Perslev', 'Akshay Pai', 'Dorit Merhof', 'Kevis-Kokitsi Maninis', 'Klaus Maier-Hein', 'Jun Ma', 'Junbo Li', 'Hongchao Li', 'Fan Li', 'Ganapathy Krishnamurthi', 'Avinash Kori', 'Simon Kohl', 'Sungwoong Kim', 'Jae-Hun Kim', 'Ildoo Kim', 'Mahendra Khened', 'Krishna Chaitanya Kaluva', 'Fucang Jia', 'Paul Jäger', 'Fabian Isensee', 'Christian Igel', 'Xu Han', 'Xavier Giró-i-Nieto', 'Bogdan Georgescu', 'Erik B. Dam', 'Lei Bi', 'Míriam Bellver', 'Woong Bae', 'Michela Antonelli', 'Zhiheng Zhang', 'Benedikt Wiestler', 'Jan Kirschke', 'Jana Lipková', 'Xiaobin Hu', 'Suprosanna Shit', 'Johannes C. Paetzold', 'Florian Kofler', 'Fernando Navarro', 'Ivan Ezhov', 'Refael Vivantik', 'Michal Marianne Amitai', 'Alexandre Hostettler', 'Wieland Sommer', 'Julian Walter Holch', 'Adi Szeskin', 'Georgios Kaissis', 'Eugene Vorontsov', 'Hongwei Bran Li', 'Patrick Christ', 'Naama Lev-Cohain', 'Avi Ben-Cohen', 'Fabian Lohöfer', 'Christian Hülsemeyer', 'Anjany Sekuboyina', 'Andrea Schenk', 'Karsten Roth', 'Markus Rempfler', 'Jin Qi', 'Xiaojuan Qi', 'Jan Hendrik Moltz', 'Hans Meine', 'John Lowengrub', 'Xiaomeng Li', 'Jürgen Hesser', 'Pheng-Ann Heng', 'Chi-Wing Fu', 'Grzegorz Chlebus', 'Rickmer Braren', 'Marie Piraud', 'Hayit Greenspan', 'Volker Heinemann', 'Qi Dou', 'Michal Drozdzal', 'Marcel Beetz', 'Leo Joskowicz', 'Hao Chen', 'Bram van Ginneken', 'Samuel Kadoury', 'Patrick Bilic', 'Felix Gruen', 'Colin Jacobs', 'An Tang', 'Tomasz Konopczynski', 'Luc Soler', 'Jacob Sosna', 'Gabriel Efrain Humpire Mamani', 'Gabriel Chartrand', 'Florian Ettlinger', 'Felix Hofmann'] | 2019-01-13 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [-3.20153236e-01 -4.82427031e-02 -3.49774480e-01 -3.31907123e-02
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e352a141-9b8e-4a3b-8770-d5434bb6f467 | findings-of-the-second-shared-task-on | 1710.07177 | null | http://arxiv.org/abs/1710.07177v1 | http://arxiv.org/pdf/1710.07177v1.pdf | Findings of the Second Shared Task on Multimodal Machine Translation and Multilingual Image Description | We present the results from the second shared task on multimodal machine
translation and multilingual image description. Nine teams submitted 19 systems
to two tasks. The multimodal translation task, in which the source sentence is
supplemented by an image, was extended with a new language (French) and two new
test sets. The multilingual image description task was changed such that at
test time, only the image is given. Compared to last year, multimodal systems
improved, but text-only systems remain competitive. | ['Loïc Barrault', 'Desmond Elliott', 'Lucia Specia', 'Stella Frank', 'Fethi Bougares'] | 2017-10-19 | findings-of-the-second-shared-task-on-1 | https://aclanthology.org/W17-4718 | https://aclanthology.org/W17-4718.pdf | ws-2017-9 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 4.60033566e-01 -1.17878526e-01 -8.51311162e-02 -4.76440132e-01
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22b61cd0-734c-4362-a686-8b25b6d6ec81 | transdocanalyser-a-framework-for-offline-semi | 2306.02142 | null | https://arxiv.org/abs/2306.02142v1 | https://arxiv.org/pdf/2306.02142v1.pdf | TransDocAnalyser: A Framework for Offline Semi-structured Handwritten Document Analysis in the Legal Domain | State-of-the-art offline Optical Character Recognition (OCR) frameworks perform poorly on semi-structured handwritten domain-specific documents due to their inability to localize and label form fields with domain-specific semantics. Existing techniques for semi-structured document analysis have primarily used datasets comprising invoices, purchase orders, receipts, and identity-card documents for benchmarking. In this work, we build the first semi-structured document analysis dataset in the legal domain by collecting a large number of First Information Report (FIR) documents from several police stations in India. This dataset, which we call the FIR dataset, is more challenging than most existing document analysis datasets, since it combines a wide variety of handwritten text with printed text. We also propose an end-to-end framework for offline processing of handwritten semi-structured documents, and benchmark it on our novel FIR dataset. Our framework used Encoder-Decoder architecture for localizing and labelling the form fields and for recognizing the handwritten content. The encoder consists of Faster-RCNN and Vision Transformers. Further the Transformer-based decoder architecture is trained with a domain-specific tokenizer. We also propose a post-correction method to handle recognition errors pertaining to the domain-specific terms. Our proposed framework achieves state-of-the-art results on the FIR dataset outperforming several existing models | ['Saptarshi Ghosh', 'Gaurav Harit', 'Sagar Chakraborty'] | 2023-06-03 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [ 5.26485562e-01 -5.55943668e-01 -4.51929774e-03 -6.42705023e-01
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6f341d8f-6c72-4310-8fbe-9672e42fa81e | lile-look-in-depth-before-looking-elsewhere-a | 2203.01445 | null | https://arxiv.org/abs/2203.01445v2 | https://arxiv.org/pdf/2203.01445v2.pdf | LILE: Look In-Depth before Looking Elsewhere -- A Dual Attention Network using Transformers for Cross-Modal Information Retrieval in Histopathology Archives | The volume of available data has grown dramatically in recent years in many applications. Furthermore, the age of networks that used multiple modalities separately has practically ended. Therefore, enabling bidirectional cross-modality data retrieval capable of processing has become a requirement for many domains and disciplines of research. This is especially true in the medical field, as data comes in a multitude of types, including various types of images and reports as well as molecular data. Most contemporary works apply cross attention to highlight the essential elements of an image or text in relation to the other modalities and try to match them together. However, regardless of their importance in their own modality, these approaches usually consider features of each modality equally. In this study, self-attention as an additional loss term will be proposed to enrich the internal representation provided into the cross attention module. This work suggests a novel architecture with a new loss term to help represent images and texts in the joint latent space. Experiment results on two benchmark datasets, i.e. MS-COCO and ARCH, show the effectiveness of the proposed method. | ['H. R Tizhoosh', 'Danial Maleki'] | 2022-03-02 | null | null | null | null | ['cross-modal-information-retrieval'] | ['miscellaneous'] | [ 3.17443162e-01 -2.96024948e-01 -3.06868136e-01 -2.44014278e-01
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2bb81444-d8c1-401a-8ce8-604d3301b85a | are-diffusion-models-vision-and-language | 2305.16397 | null | https://arxiv.org/abs/2305.16397v1 | https://arxiv.org/pdf/2305.16397v1.pdf | Are Diffusion Models Vision-And-Language Reasoners? | Text-conditioned image generation models have recently shown immense qualitative success using denoising diffusion processes. However, unlike discriminative vision-and-language models, it is a non-trivial task to subject these diffusion-based generative models to automatic fine-grained quantitative evaluation of high-level phenomena such as compositionality. Towards this goal, we perform two innovations. First, we transform diffusion-based models (in our case, Stable Diffusion) for any image-text matching (ITM) task using a novel method called DiffusionITM. Second, we introduce the Generative-Discriminative Evaluation Benchmark (GDBench) benchmark with 7 complex vision-and-language tasks, bias evaluation and detailed analysis. We find that Stable Diffusion + DiffusionITM is competitive on many tasks and outperforms CLIP on compositional tasks like like CLEVR and Winoground. We further boost its compositional performance with a transfer setup by fine-tuning on MS-COCO while retaining generative capabilities. We also measure the stereotypical bias in diffusion models, and find that Stable Diffusion 2.1 is, for the most part, less biased than Stable Diffusion 1.5. Overall, our results point in an exciting direction bringing discriminative and generative model evaluation closer. We will release code and benchmark setup soon. | ['Siva Reddy', 'Christopher Pal', 'Vikram Voleti', 'Elinor Poole-Dayan', 'Benno Krojer'] | 2023-05-25 | null | null | null | null | ['text-matching'] | ['natural-language-processing'] | [ 3.12748104e-01 -1.90786645e-01 1.93764940e-01 -3.24426174e-01
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4f5dde2d-799d-4224-b85d-0deada0b1879 | granngan-graph-annotation-generative | 2212.00449 | null | https://arxiv.org/abs/2212.00449v1 | https://arxiv.org/pdf/2212.00449v1.pdf | GrannGAN: Graph annotation generative adversarial networks | We consider the problem of modelling high-dimensional distributions and generating new examples of data with complex relational feature structure coherent with a graph skeleton. The model we propose tackles the problem of generating the data features constrained by the specific graph structure of each data point by splitting the task into two phases. In the first it models the distribution of features associated with the nodes of the given graph, in the second it complements the edge features conditionally on the node features. We follow the strategy of implicit distribution modelling via generative adversarial network (GAN) combined with permutation equivariant message passing architecture operating over the sets of nodes and edges. This enables generating the feature vectors of all the graph objects in one go (in 2 phases) as opposed to a much slower one-by-one generations of sequential models, prevents the need for expensive graph matching procedures usually needed for likelihood-based generative models, and uses efficiently the network capacity by being insensitive to the particular node ordering in the graph representation. To the best of our knowledge, this is the first method that models the feature distribution along the graph skeleton allowing for generations of annotated graphs with user specified structures. Our experiments demonstrate the ability of our model to learn complex structured distributions through quantitative evaluation over three annotated graph datasets. | ['Alexandros Kalousis', 'Magda Gregorova', 'Yoann Boget'] | 2022-12-01 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 3.16645652e-01 7.36829996e-01 2.23105133e-01 -3.45952481e-01
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ca46b1b9-b698-401f-9ade-aaf7ad65886d | optimal-prediction-using-expert-advice-and | 2302.13849 | null | https://arxiv.org/abs/2302.13849v2 | https://arxiv.org/pdf/2302.13849v2.pdf | Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension | A classical result in online learning characterizes the optimal mistake bound achievable by deterministic learners using the Littlestone dimension (Littlestone '88). We prove an analogous result for randomized learners: we show that the optimal expected mistake bound in learning a class $\mathcal{H}$ equals its randomized Littlestone dimension, which is the largest $d$ for which there exists a tree shattered by $\mathcal{H}$ whose average depth is $2d$. We further study optimal mistake bounds in the agnostic case, as a function of the number of mistakes made by the best function in $\mathcal{H}$, denoted by $k$. We show that the optimal randomized mistake bound for learning a class with Littlestone dimension $d$ is $k + \Theta (\sqrt{k d} + d )$. This also implies an optimal deterministic mistake bound of $2k + O (\sqrt{k d} + d )$, thus resolving an open question which was studied by Auer and Long ['99]. As an application of our theory, we revisit the classical problem of prediction using expert advice: about 30 years ago Cesa-Bianchi, Freund, Haussler, Helmbold, Schapire and Warmuth studied prediction using expert advice, provided that the best among the $n$ experts makes at most $k$ mistakes, and asked what are the optimal mistake bounds. Cesa-Bianchi, Freund, Helmbold, and Warmuth ['93, '96] provided a nearly optimal bound for deterministic learners, and left the randomized case as an open problem. We resolve this question by providing an optimal learning rule in the randomized case, and showing that its expected mistake bound equals half of the deterministic bound, up to negligible additive terms. This improves upon previous works by Cesa-Bianchi, Freund, Haussler, Helmbold, Schapire and Warmuth ['93, '97], by Abernethy, Langford, and Warmuth ['06], and by Br\^anzei and Peres ['19], which handled the regimes $k \ll \log n$ or $k \gg \log n$. | ['Shay Moran', 'Idan Mehalel', 'Steve Hanneke', 'Yuval Filmus'] | 2023-02-27 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [-3.10732186e-01 4.44320917e-01 5.72389318e-03 -1.63793772e-01
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98b8e1b8-968e-4af6-85f0-84bb617b3f21 | qumos-a-framework-for-preserving-security-of | 2304.11511 | null | https://arxiv.org/abs/2304.11511v1 | https://arxiv.org/pdf/2304.11511v1.pdf | QuMoS: A Framework for Preserving Security of Quantum Machine Learning Model | Security has always been a critical issue in machine learning (ML) applications. Due to the high cost of model training -- such as collecting relevant samples, labeling data, and consuming computing power -- model-stealing attack is one of the most fundamental but vitally important issues. When it comes to quantum computing, such a quantum machine learning (QML) model-stealing attack also exists and it is even more severe because the traditional encryption method can hardly be directly applied to quantum computation. On the other hand, due to the limited quantum computing resources, the monetary cost of training QML model can be even higher than classical ones in the near term. Therefore, a well-tuned QML model developed by a company can be delegated to a quantum cloud provider as a service to be used by ordinary users. In this case, the QML model will be leaked if the cloud provider is under attack. To address such a problem, we propose a novel framework, namely QuMoS, to preserve model security. Instead of applying encryption algorithms, we propose to distribute the QML model to multiple physically isolated quantum cloud providers. As such, even if the adversary in one provider can obtain a partial model, the information of the full model is maintained in the QML service company. Although promising, we observed an arbitrary model design under distributed settings cannot provide model security. We further developed a reinforcement learning-based security engine, which can automatically optimize the model design under the distributed setting, such that a good trade-off between model performance and security can be made. Experimental results on four datasets show that the model design proposed by QuMoS can achieve a close accuracy to the model designed with neural architecture search under centralized settings while providing the highest security than the baselines. | ['Weiwen Jiang', 'Elizabeth Iwasawa', 'Blake Gage', 'Zhirui Hu', 'Jinyang Li', 'Zhepeng Wang'] | 2023-04-23 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 4.77932207e-02 2.95528490e-02 -1.32572949e-01 -2.20337540e-01
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726a87e3-a271-4749-9b11-854eda37f227 | skill-based-few-shot-selection-for-in-context | 2305.14210 | null | https://arxiv.org/abs/2305.14210v1 | https://arxiv.org/pdf/2305.14210v1.pdf | Skill-Based Few-Shot Selection for In-Context Learning | In-Context learning is the paradigm that adapts large language models to downstream tasks by providing a few examples. Few-shot selection -- selecting appropriate examples for each test instance separately -- is important for in-context learning. In this paper, we propose Skill-KNN, a skill-based few-shot selection method for in-context learning. The key advantages of Skill-KNN include: (1) it addresses the problem that existing methods based on pre-trained embeddings can be easily biased by surface natural language features that are not important for the target task; (2) it does not require training or fine-tuning of any models, making it suitable for frequently expanding or changing example banks. The key insight is to optimize the inputs fed into the embedding model, rather than tuning the model itself. Technically, Skill-KNN generates the skill-based representations for each test case and candidate example by utilizing a pre-processing few-shot prompting, thus eliminating unimportant surface features. Experimental results across four cross-domain semantic parsing tasks and four backbone models show that Skill-KNN significantly outperforms existing methods. | ['Jian-Guang Lou', 'Weizhu Chen', 'Nanning Zheng', 'Bei Chen', 'Qiang Fu', 'Zeqi Lin', 'Bo Zhou', 'Shengnan An'] | 2023-05-23 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 3.35583270e-01 1.61887184e-01 -3.31491798e-01 -7.48532951e-01
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7de7e6c6-1a33-4a71-b530-ed2c5fb83a96 | am-i-fit-for-this-physical-activity-neural | 2103.12095 | null | https://arxiv.org/abs/2103.12095v2 | https://arxiv.org/pdf/2103.12095v2.pdf | Am I fit for this physical activity? Neural embedding of physical conditioning from inertial sensors | Inertial Measurement Unit (IMU) sensors are present in everyday devices such as smartphones and fitness watches. As a result, the array of health-related research and applications that tap onto this data has been growing, but little attention has been devoted to the prediction of an individual's heart rate (HR) from IMU data, when undergoing a physical activity. Would that be even possible? If so, this could be used to design personalized sets of aerobic exercises, for instance. In this work, we show that it is viable to obtain accurate HR predictions from IMU data using Recurrent Neural Networks, provided only access to HR and IMU data from a short-lived, previously executed activity. We propose a novel method for initializing an RNN's hidden state vectors, using a specialized network that attempts to extract an embedding of the physical conditioning (PCE) of a subject. We show that using a discriminator in the training phase to help the model learn whether two PCEs belong to the same individual further reduces the prediction error. We evaluate the proposed model when predicting the HR of 23 subjects performing a variety of physical activities from IMU data available in public datasets (PAMAP2, PPG-DaLiA). For comparison, we use as baselines the only model specifically proposed for this task and an adapted state-of-the-art model for Human Activity Recognition (HAR), a closely related task. Our method, PCE-LSTM, yields over 10% lower mean absolute error. We demonstrate empirically that this error reduction is in part due to the use of the PCE. Last, we use the two datasets (PPG-DaLiA, WESAD) to show that PCE-LSTM can also be successfully applied when photoplethysmography (PPG) sensors are available, outperforming the state-of-the-art deep learning baselines by more than 30%. | ['Fabricio Murai', 'Davi Pedrosa de Aguiar'] | 2021-03-22 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 6.32676601e-01 2.07999408e-01 -2.71523893e-01 -9.54628289e-02
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bdbf0694-f772-4201-b80c-ac1050f43bf2 | learning-from-explicit-and-implicit | null | null | https://aclanthology.org/D16-1029 | https://aclanthology.org/D16-1029.pdf | Learning from Explicit and Implicit Supervision Jointly For Algebra Word Problems | null | ['Wen-tau Yih', 'Kai-Wei Chang', 'Ming-Wei Chang', 'Shyam Upadhyay'] | 2016-11-01 | null | null | null | emnlp-2016-11 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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b93fe4d3-a181-45a2-aed6-ed654abb9247 | conical-classification-for-efficient-one | null | null | https://aclanthology.org/2021.findings-emnlp.143 | https://aclanthology.org/2021.findings-emnlp.143.pdf | Conical Classification For Efficient One-Class Topic Determination | As the Internet grows in size, so does the amount of text based information that exists. For many application spaces it is paramount to isolate and identify texts that relate to a particular topic. While one-class classification would be ideal for such analysis, there is a relative lack of research regarding efficient approaches with high predictive power. By noting that the range of documents we wish to identify can be represented as positive linear combinations of the Vector Space Model representing our text, we propose Conical classification, an approach that allows us to identify if a document is of a particular topic in a computationally efficient manner. We also propose Normal Exclusion, a modified version of Bi-Normal Separation that makes it more suitable within the one-class classification context. We show in our analysis that our approach not only has higher predictive power on our datasets, but is also faster to compute. | ['Sameer Khanna'] | null | null | null | null | findings-emnlp-2021-11 | ['one-class-classification'] | ['miscellaneous'] | [ 3.20975155e-01 -2.85080254e-01 -3.71154457e-01 -1.56644896e-01
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82c00ca0-c08b-4a5e-8689-081d86ead713 | temporal-consistent-3d-lidar-representation | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Nunes_Temporal_Consistent_3D_LiDAR_Representation_Learning_for_Semantic_Perception_in_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Nunes_Temporal_Consistent_3D_LiDAR_Representation_Learning_for_Semantic_Perception_in_CVPR_2023_paper.pdf | Temporal Consistent 3D LiDAR Representation Learning for Semantic Perception in Autonomous Driving | Semantic perception is a core building block in autonomous driving, since it provides information about the drivable space and location of other traffic participants. For learning-based perception, often a large amount of diverse training data is necessary to achieve high performance. Data labeling is usually a bottleneck for developing such methods, especially for dense prediction tasks, e.g., semantic segmentation or panoptic segmentation. For 3D LiDAR data, the annotation process demands even more effort than for images. Especially in autonomous driving, point clouds are sparse, and objects appearance depends on its distance from the sensor, making it harder to acquire large amounts of labeled training data. This paper aims at taking an alternative path proposing a self-supervised representation learning method for 3D LiDAR data. Our approach exploits the vehicle motion to match objects across time viewed in different scans. We then train a model to maximize the point-wise feature similarities from points of the associated object in different scans, which enables to learn a consistent representation across time. The experimental results show that our approach performs better than previous state-of-the-art self-supervised representation learning methods when fine-tuning to different downstream tasks. We furthermore show that with only 10% of labeled data, a network pre-trained with our approach can achieve better performance than the same network trained from scratch with all labels for semantic segmentation on SemanticKITTI. | ['Cyrill Stachniss', 'Jens Behley', 'Xieyuanli Chen', 'Rodrigo Marcuzzi', 'Louis Wiesmann', 'Lucas Nunes'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['panoptic-segmentation'] | ['computer-vision'] | [ 8.51318538e-02 -1.02331892e-01 -4.48659003e-01 -9.09930170e-01
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5184e874-868e-447a-8b2d-d7c0a7e5fcc7 | semi-supervised-2d-human-pose-estimation | 2303.04346 | null | https://arxiv.org/abs/2303.04346v1 | https://arxiv.org/pdf/2303.04346v1.pdf | Semi-Supervised 2D Human Pose Estimation Driven by Position Inconsistency Pseudo Label Correction Module | In this paper, we delve into semi-supervised 2D human pose estimation. The previous method ignored two problems: (i) When conducting interactive training between large model and lightweight model, the pseudo label of lightweight model will be used to guide large models. (ii) The negative impact of noise pseudo labels on training. Moreover, the labels used for 2D human pose estimation are relatively complex: keypoint category and keypoint position. To solve the problems mentioned above, we propose a semi-supervised 2D human pose estimation framework driven by a position inconsistency pseudo label correction module (SSPCM). We introduce an additional auxiliary teacher and use the pseudo labels generated by the two teacher model in different periods to calculate the inconsistency score and remove outliers. Then, the two teacher models are updated through interactive training, and the student model is updated using the pseudo labels generated by two teachers. To further improve the performance of the student model, we use the semi-supervised Cut-Occlude based on pseudo keypoint perception to generate more hard and effective samples. In addition, we also proposed a new indoor overhead fisheye human keypoint dataset WEPDTOF-Pose. Extensive experiments demonstrate that our method outperforms the previous best semi-supervised 2D human pose estimation method. We will release the code and dataset at https://github.com/hlz0606/SSPCM. | ['Jieping Ye', 'Weihong Deng', 'Xiangang Li', 'Yue Yang', 'Hongbo Tian', 'Yulong Li', 'Linzhi Huang'] | 2023-03-08 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Huang_Semi-Supervised_2D_Human_Pose_Estimation_Driven_by_Position_Inconsistency_Pseudo_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Huang_Semi-Supervised_2D_Human_Pose_Estimation_Driven_by_Position_Inconsistency_Pseudo_CVPR_2023_paper.pdf | cvpr-2023-1 | ['2d-human-pose-estimation', 'pseudo-label'] | ['computer-vision', 'miscellaneous'] | [-2.36807689e-01 1.16627514e-01 -1.64206196e-02 -2.69888759e-01
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821be728-a65c-485b-a4ca-9043f36d9f7f | detecting-drug-drug-interactions-using | 1903.04571 | null | https://arxiv.org/abs/1903.04571v2 | https://arxiv.org/pdf/1903.04571v2.pdf | Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures | Drug-drug interactions are preventable causes of medical injuries and often result in doctor and emergency room visits. Computational techniques can be used to predict potential drug-drug interactions. We approach the drug-drug interaction prediction problem as a link prediction problem and present two novel methods for drug-drug interaction prediction based on artificial neural networks and factor propagation over graph nodes: adjacency matrix factorization (AMF) and adjacency matrix factorization with propagation (AMFP). We conduct a retrospective analysis by training our models on a previous release of the DrugBank database with 1,141 drugs and 45,296 drug-drug interactions and evaluate the results on a later version of DrugBank with 1,440 drugs and 248,146 drug-drug interactions. Additionally, we perform a holdout analysis using DrugBank. We report an area under the receiver operating characteristic curve score of 0.807 and 0.990 for the retrospective and holdout analyses respectively. Finally, we create an ensemble-based classifier using AMF, AMFP, and existing link prediction methods and obtain an area under the receiver operating characteristic curve of 0.814 and 0.991 for the retrospective and the holdout analyses. We demonstrate that AMF and AMFP provide state of the art results compared to existing methods and that the ensemble-based classifier improves the performance by combining various predictors. These results suggest that AMF, AMFP, and the proposed ensemble-based classifier can provide important information during drug development and regarding drug prescription given only partial or noisy data. These methods can also be used to solve other link prediction problems. Drug embeddings (compressed representations) created when training our models using the interaction network have been made public. | ['Guy Shtar', 'Lior Rokach', 'Bracha Shapira'] | 2019-03-11 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [ 5.37343025e-02 1.93934008e-01 -6.91844523e-01 4.16438008e-04
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1e45c9f7-d189-4281-8afc-09abc3f9a69e | dct-centered-temporal-relation-extraction | null | null | https://aclanthology.org/2022.coling-1.182 | https://aclanthology.org/2022.coling-1.182.pdf | DCT-Centered Temporal Relation Extraction | Most previous work on temporal relation extraction only focused on extracting the temporal relations among events or suffered from the issue of different expressions of events, timexes and Document Creation Time (DCT). Moreover, DCT can act as a hub to semantically connect the other events and timexes in a document. Unfortunately, previous work cannot benefit from such critical information. To address the above issues, we propose a unified DCT-centered Temporal Relation Extraction model DTRE to identify the relations among events, timexes and DCT. Specifically, sentence-style DCT representation is introduced to address the first issue and unify event expressions, timexes and DCT. Then, a DCT-aware graph is applied to obtain their contextual structural representations. Furthermore, a DCT-anchoring multi-task learning framework is proposed to jointly predict three types of temporal relations in a batch. Finally, we apply a DCT-guided global inference to further enhance the global consistency among different relations. Experimental results on three datasets show that our DTRE outperforms several SOTA baselines on E-E, E-T and E-D significantly. | ['Sheng Xu', 'Peifeng Li', 'Liang Wang'] | null | null | null | null | coling-2022-10 | ['temporal-relation-extraction', 'temporal-relation-classification'] | ['natural-language-processing', 'natural-language-processing'] | [-1.21267036e-01 -3.17937136e-01 -5.29514074e-01 -5.36212862e-01
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d610fa83-c4de-4e6b-a41e-8a9c03af793d | pre-trained-and-attention-based-neural | 2004.01940 | null | https://arxiv.org/abs/2004.01940v1 | https://arxiv.org/pdf/2004.01940v1.pdf | Pre-Trained and Attention-Based Neural Networks for Building Noetic Task-Oriented Dialogue Systems | The NOESIS II challenge, as the Track 2 of the 8th Dialogue System Technology Challenges (DSTC 8), is the extension of DSTC 7. This track incorporates new elements that are vital for the creation of a deployed task-oriented dialogue system. This paper describes our systems that are evaluated on all subtasks under this challenge. We study the problem of employing pre-trained attention-based network for multi-turn dialogue systems. Meanwhile, several adaptation methods are proposed to adapt the pre-trained language models for multi-turn dialogue systems, in order to keep the intrinsic property of dialogue systems. In the released evaluation results of Track 2 of DSTC 8, our proposed models ranked fourth in subtask 1, third in subtask 2, and first in subtask 3 and subtask 4 respectively. | ['Yu-Ping Ruan', 'Zhen-Hua Ling', 'Quan Liu', 'Jia-Chen Gu', 'Xiaodan Zhu', 'Tianda Li'] | 2020-04-04 | null | null | null | null | ['conversation-disentanglement'] | ['natural-language-processing'] | [-1.99104816e-01 7.36365139e-01 3.34494948e-01 -3.62823755e-01
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de8dddd5-b0a5-4d20-9435-d87766cf64b7 | lstm-based-system-call-language-modeling-and | 1611.01726 | null | http://arxiv.org/abs/1611.01726v1 | http://arxiv.org/pdf/1611.01726v1.pdf | LSTM-Based System-Call Language Modeling and Robust Ensemble Method for Designing Host-Based Intrusion Detection Systems | In computer security, designing a robust intrusion detection system is one of
the most fundamental and important problems. In this paper, we propose a
system-call language-modeling approach for designing anomaly-based host
intrusion detection systems. To remedy the issue of high false-alarm rates
commonly arising in conventional methods, we employ a novel ensemble method
that blends multiple thresholding classifiers into a single one, making it
possible to accumulate 'highly normal' sequences. The proposed system-call
language model has various advantages leveraged by the fact that it can learn
the semantic meaning and interactions of each system call that existing methods
cannot effectively consider. Through diverse experiments on public benchmark
datasets, we demonstrate the validity and effectiveness of the proposed method.
Moreover, we show that our model possesses high portability, which is one of
the key aspects of realizing successful intrusion detection systems. | ['Jangho Lee', 'Yunheung Paek', 'Gyuwan Kim', 'Sungroh Yoon', 'Hayoon Yi'] | 2016-11-06 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [ 1.36246577e-01 -7.06544876e-01 -2.43886292e-01 -2.83774018e-01
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b7e1b707-e9bf-40e1-883d-96d792cfcf07 | local-connectivity-based-density-estimation | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Shin_Local_Connectivity-Based_Density_Estimation_for_Face_Clustering_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Shin_Local_Connectivity-Based_Density_Estimation_for_Face_Clustering_CVPR_2023_paper.pdf | Local Connectivity-Based Density Estimation for Face Clustering | Recent graph-based face clustering methods predict the connectivity of enormous edges, including false positive edges that link nodes with different classes. However, those false positive edges, which connect negative node pairs, have the risk of integration of different clusters when their connectivity is incorrectly estimated. This paper proposes a novel face clustering method to address this problem. The proposed clustering method employs density-based clustering, which maintains edges that have higher density. For this purpose, we propose a reliable density estimation algorithm based on local connectivity between K nearest neighbors (KNN). We effectively exclude negative pairs from the KNN graph based on the reliable density while maintaining sufficient positive pairs. Furthermore, we develop a pairwise connectivity estimation network to predict the connectivity of the selected edges. Experimental results demonstrate that the proposed clustering method significantly outperforms the state-of-the-art clustering methods on large-scale face clustering datasets and fashion image clustering datasets. Our code is available at https://github.com/illian01/LCE-PCENet | ['Yeong Jun Koh', 'Daehyun Kim', 'Jong-Hyeon Baek', 'Hyunseop Kim', 'Hyo-Jun Lee', 'Junho Shin'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['image-clustering', 'face-clustering', 'connectivity-estimation'] | ['computer-vision', 'computer-vision', 'graphs'] | [-4.63216066e-01 1.21935479e-01 -3.23170155e-01 -4.77550864e-01
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6aafbfb8-d929-4a9b-b1ae-02224c1682f2 | crowd-counting-by-adapting-convolutional | 1611.06748 | null | http://arxiv.org/abs/1611.06748v1 | http://arxiv.org/pdf/1611.06748v1.pdf | Crowd Counting by Adapting Convolutional Neural Networks with Side Information | Computer vision tasks often have side information available that is helpful
to solve the task. For example, for crowd counting, the camera perspective
(e.g., camera angle and height) gives a clue about the appearance and scale of
people in the scene. While side information has been shown to be useful for
counting systems using traditional hand-crafted features, it has not been fully
utilized in counting systems based on deep learning. In order to incorporate
the available side information, we propose an adaptive convolutional neural
network (ACNN), where the convolutional filter weights adapt to the current
scene context via the side information. In particular, we model the filter
weights as a low-dimensional manifold, parametrized by the side information,
within the high-dimensional space of filter weights. With the help of side
information and adaptive weights, the ACNN can disentangle the variations
related to the side information, and extract discriminative features related to
the current context. Since existing crowd counting datasets do not contain
ground-truth side information, we collect a new dataset with the ground-truth
camera angle and height as the side information. On experiments in crowd
counting, the ACNN improves counting accuracy compared to a plain CNN with a
similar number of parameters. We also apply ACNN to image deconvolution to show
its potential effectiveness on other computer vision applications. | ['Debarun Dhar', 'Antoni B. Chan', 'Di Kang'] | 2016-11-21 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [-2.84290314e-01 -5.47742248e-01 3.51939380e-01 -5.27495861e-01
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f262a60f-10c3-48ed-98ba-3b397a8feec2 | geometric-relational-embeddings-a-survey | 2304.11949 | null | https://arxiv.org/abs/2304.11949v1 | https://arxiv.org/pdf/2304.11949v1.pdf | Geometric Relational Embeddings: A Survey | Geometric relational embeddings map relational data as geometric objects that combine vector information suitable for machine learning and structured/relational information for structured/relational reasoning, typically in low dimensions. Their preservation of relational structures and their appealing properties and interpretability have led to their uptake for tasks such as knowledge graph completion, ontology and hierarchy reasoning, logical query answering, and hierarchical multi-label classification. We survey methods that underly geometric relational embeddings and categorize them based on (i) the embedding geometries that are used to represent the data; and (ii) the relational reasoning tasks that they aim to improve. We identify the desired properties (i.e., inductive biases) of each kind of embedding and discuss some potential future work. | ['Steffen Staab', 'Shirui Pan', 'Michael Cochez', 'Yunjie He', 'Ming Jin', 'Mojtaba Nayyeri', 'Bo Xiong'] | 2023-04-24 | null | null | null | null | ['knowledge-graph-completion', 'relational-reasoning'] | ['knowledge-base', 'natural-language-processing'] | [-9.86482948e-02 8.08132470e-01 -6.36946797e-01 -4.77385849e-01
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1aeaf599-6ce8-4893-8642-9a4fa000b98b | weak-multi-view-supervision-for-surface | 2105.01388 | null | https://arxiv.org/abs/2105.01388v1 | https://arxiv.org/pdf/2105.01388v1.pdf | Weak Multi-View Supervision for Surface Mapping Estimation | We propose a weakly-supervised multi-view learning approach to learn category-specific surface mapping without dense annotations. We learn the underlying surface geometry of common categories, such as human faces, cars, and airplanes, given instances from those categories. While traditional approaches solve this problem using extensive supervision in the form of pixel-level annotations, we take advantage of the fact that pixel-level UV and mesh predictions can be combined with 3D reprojections to form consistency cycles. As a result of exploiting these cycles, we can establish a dense correspondence mapping between image pixels and the mesh acting as a self-supervisory signal, which in turn helps improve our overall estimates. Our approach leverages information from multiple views of the object to establish additional consistency cycles, thus improving surface mapping understanding without the need for explicit annotations. We also propose the use of deformation fields for predictions of an instance specific mesh. Given the lack of datasets providing multiple images of similar object instances from different viewpoints, we generate and release a multi-view ShapeNet Cars and Airplanes dataset created by rendering ShapeNet meshes using a 360 degree camera trajectory around the mesh. For the human faces category, we process and adapt an existing dataset to a multi-view setup. Through experimental evaluations, we show that, at test time, our method can generate accurate variations away from the mean shape, is multi-view consistent, and performs comparably to fully supervised approaches. | ['Stefan Holzer', 'Matteo Munaro', 'Rodrigo Ortiz Cayon', 'Srinivas Rao', 'Aidas Liaudanskas', 'Nishant Rai'] | 2021-05-04 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [ 2.36951664e-01 2.47494265e-01 -3.85731272e-02 -5.94674766e-01
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ebf45e6d-92b9-46ee-9da4-b2fe07de7939 | a-sequential-quadratic-programming-approach | 2203.16478 | null | https://arxiv.org/abs/2203.16478v2 | https://arxiv.org/pdf/2203.16478v2.pdf | A Sequential Quadratic Programming Approach to the Solution of Open-Loop Generalized Nash Equilibria | Dynamic games can be an effective approach to modeling interactive behavior between multiple non-cooperative agents and they provide a theoretical framework for simultaneous prediction and control in such scenarios. In this work, we propose a numerical method for the solution of local generalized Nash equilibria (GNE) for the class of open-loop general-sum dynamic games for agents with nonlinear dynamics and constraints. In particular, we formulate a sequential quadratic programming (SQP) approach which requires only the solution of a single convex quadratic program at each iteration. Central to the robustness of our approach is a non-monotonic line search method and a novel merit function for SQP step acceptance. We show that our method achieves linear convergence in the neighborhood of local GNE and we derive an update rule for the merit function which helps to improve convergence from a larger set of initial conditions. We demonstrate the effectiveness of the algorithm in the context of car racing, where we show up to 32\% improvement of success rate when comparing against a state-of-the-art solution approach for dynamic games. \url{https://github.com/zhu-edward/DGSQP}. | ['Francesco Borrelli', 'Edward L. Zhu'] | 2022-03-30 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [-2.39909142e-01 5.09911180e-02 2.06399336e-02 2.42087275e-01
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280f347c-a2ab-469e-9369-960a766b7269 | gorilla-large-language-model-connected-with | 2305.15334 | null | https://arxiv.org/abs/2305.15334v1 | https://arxiv.org/pdf/2305.15334v1.pdf | Gorilla: Large Language Model Connected with Massive APIs | Large Language Models (LLMs) have seen an impressive wave of advances recently, with models now excelling in a variety of tasks, such as mathematical reasoning and program synthesis. However, their potential to effectively use tools via API calls remains unfulfilled. This is a challenging task even for today's state-of-the-art LLMs such as GPT-4, largely due to their inability to generate accurate input arguments and their tendency to hallucinate the wrong usage of an API call. We release Gorilla, a finetuned LLaMA-based model that surpasses the performance of GPT-4 on writing API calls. When combined with a document retriever, Gorilla demonstrates a strong capability to adapt to test-time document changes, enabling flexible user updates or version changes. It also substantially mitigates the issue of hallucination, commonly encountered when prompting LLMs directly. To evaluate the model's ability, we introduce APIBench, a comprehensive dataset consisting of HuggingFace, TorchHub, and TensorHub APIs. The successful integration of the retrieval system with Gorilla demonstrates the potential for LLMs to use tools more accurately, keep up with frequently updated documentation, and consequently increase the reliability and applicability of their outputs. Gorilla's code, model, data, and demo are available at https://gorilla.cs.berkeley.edu | ['Joseph E. Gonzalez', 'Xin Wang', 'Tianjun Zhang', 'Shishir G. Patil'] | 2023-05-24 | null | null | null | null | ['program-synthesis', 'mathematical-reasoning'] | ['computer-code', 'natural-language-processing'] | [-3.09094965e-01 -9.91586521e-02 -7.84533694e-02 -1.65783793e-01
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3f831938-42b7-4d6a-94fe-4527004e3009 | explainable-cardiac-pathology-classification | 1811.03433 | null | http://arxiv.org/abs/1811.03433v2 | http://arxiv.org/pdf/1811.03433v2.pdf | Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow | We propose a method to classify cardiac pathology based on a novel approach
to extract image derived features to characterize the shape and motion of the
heart. An original semi-supervised learning procedure, which makes efficient
use of a large amount of non-segmented images and a small amount of images
segmented manually by experts, is developed to generate pixel-wise apparent
flow between two time points of a 2D+t cine MRI image sequence. Combining the
apparent flow maps and cardiac segmentation masks, we obtain a local apparent
flow corresponding to the 2D motion of myocardium and ventricular cavities.
This leads to the generation of time series of the radius and thickness of
myocardial segments to represent cardiac motion. These time series of motion
features are reliable and explainable characteristics of pathological cardiac
motion. Furthermore, they are combined with shape-related features to classify
cardiac pathologies. Using only nine feature values as input, we propose an
explainable, simple and flexible model for pathology classification. On ACDC
training set and testing set, the model achieves 95% and 94% respectively as
classification accuracy. Its performance is hence comparable to that of the
state-of-the-art. Comparison with various other models is performed to outline
some advantages of our model. | ['Hervé Delingette', 'Nicholas Ayache', 'Qiao Zheng'] | 2018-11-08 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 2.74351388e-01 9.97366160e-02 -7.63599668e-03 -3.50125104e-01
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7feae8fc-7bbc-4869-82db-86e1ef16e9ae | partial-advantage-estimator-for-proximal | 2301.10920 | null | https://arxiv.org/abs/2301.10920v1 | https://arxiv.org/pdf/2301.10920v1.pdf | Partial advantage estimator for proximal policy optimization | Estimation of value in policy gradient methods is a fundamental problem. Generalized Advantage Estimation (GAE) is an exponentially-weighted estimator of an advantage function similar to $\lambda$-return. It substantially reduces the variance of policy gradient estimates at the expense of bias. In practical applications, a truncated GAE is used due to the incompleteness of the trajectory, which results in a large bias during estimation. To address this challenge, instead of using the entire truncated GAE, we propose to take a part of it when calculating updates, which significantly reduces the bias resulting from the incomplete trajectory. We perform experiments in MuJoCo and $\mu$RTS to investigate the effect of different partial coefficient and sampling lengths. We show that our partial GAE approach yields better empirical results in both environments. | ['Simon Lucas', 'Greg Slabaugh', 'Yizhao Jin', 'Xiulei Song'] | 2023-01-26 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-2.86417037e-01 -1.67750850e-01 -4.80274379e-01 -1.70842648e-01
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f455902b-35db-4861-afd8-4fde9e70a6a7 | hierarchical-verbalizer-for-few-shot | 2305.16885 | null | https://arxiv.org/abs/2305.16885v1 | https://arxiv.org/pdf/2305.16885v1.pdf | Hierarchical Verbalizer for Few-Shot Hierarchical Text Classification | Due to the complex label hierarchy and intensive labeling cost in practice, the hierarchical text classification (HTC) suffers a poor performance especially when low-resource or few-shot settings are considered. Recently, there is a growing trend of applying prompts on pre-trained language models (PLMs), which has exhibited effectiveness in the few-shot flat text classification tasks. However, limited work has studied the paradigm of prompt-based learning in the HTC problem when the training data is extremely scarce. In this work, we define a path-based few-shot setting and establish a strict path-based evaluation metric to further explore few-shot HTC tasks. To address the issue, we propose the hierarchical verbalizer ("HierVerb"), a multi-verbalizer framework treating HTC as a single- or multi-label classification problem at multiple layers and learning vectors as verbalizers constrained by hierarchical structure and hierarchical contrastive learning. In this manner, HierVerb fuses label hierarchy knowledge into verbalizers and remarkably outperforms those who inject hierarchy through graph encoders, maximizing the benefits of PLMs. Extensive experiments on three popular HTC datasets under the few-shot settings demonstrate that prompt with HierVerb significantly boosts the HTC performance, meanwhile indicating an elegant way to bridge the gap between the large pre-trained model and downstream hierarchical classification tasks. Our code and few-shot dataset are publicly available at https://github.com/1KE-JI/HierVerb. | ['Baoyuan Wang', 'Jingsheng Gao', 'Yixin Lian', 'Ke Ji'] | 2023-05-26 | null | null | null | null | ['few-shot-htc-1'] | ['natural-language-processing'] | [ 1.55721813e-01 2.34191790e-01 -6.93220317e-01 -3.35194886e-01
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c35e6dc2-e2f2-4839-aa7e-30a7395cec61 | better-queries-for-aspect-category-sentiment | null | null | https://aclanthology.org/2020.ccl-1.100 | https://aclanthology.org/2020.ccl-1.100.pdf | Better Queries for Aspect-Category Sentiment Classification | Aspect-category sentiment classification (ACSC) aims to identify the sentiment polarities towards the aspect categories mentioned in a sentence. Because a sentence often mentions more than one aspect category and expresses different sentiment polarities to them, finding aspect category-related information from the sentence is the key challenge to accurately recognize the sentiment polarity. Most previous models take both sentence and aspect category as input and query aspect category-related information based on the aspect category. However, these models represent the aspect category as a context-independent vector called aspect embedding, which may not be effective enough as a query. In this paper, we propose two contextualized aspect category representations, Contextualized Aspect Vector (CAV) and Contextualized Aspect Matrix (CAM). Specifically, we use the coarse aspect category-related information found by the aspect category detection task to generate CAV or CAM. Then the CAV or CAM as queries are used to search for fine-grained aspect category-related information like aspect embedding by aspect-category sentiment classification models. In experiments, we integrate the proposed CAV and CAM into several representative aspect embedding-based aspect-category sentiment classification models. Experimental results on the SemEval-2014 Restaurant Review dataset and the Multi-Aspect Multi-Sentiment dataset demonstrate the effectiveness of CAV and CAM. | ['Wu Xiaohui', 'Xu Siqi', 'Luo Jinchang', 'Zhong Huiqiang', 'Zhong Sheng-hua', 'Yin Cunxiang', 'Li Yuncong'] | null | null | null | null | ccl-2020-10 | ['aspect-category-detection'] | ['natural-language-processing'] | [ 1.24169432e-01 -1.83181971e-01 -5.26366949e-01 -6.10477984e-01
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2cacea04-2f17-4091-90c9-9e3a897a1dff | sign-and-basis-invariant-networks-for | 2202.13013 | null | https://arxiv.org/abs/2202.13013v4 | https://arxiv.org/pdf/2202.13013v4.pdf | Sign and Basis Invariant Networks for Spectral Graph Representation Learning | We introduce SignNet and BasisNet -- new neural architectures that are invariant to two key symmetries displayed by eigenvectors: (i) sign flips, since if $v$ is an eigenvector then so is $-v$; and (ii) more general basis symmetries, which occur in higher dimensional eigenspaces with infinitely many choices of basis eigenvectors. We prove that under certain conditions our networks are universal, i.e., they can approximate any continuous function of eigenvectors with the desired invariances. When used with Laplacian eigenvectors, our networks are provably more expressive than existing spectral methods on graphs; for instance, they subsume all spectral graph convolutions, certain spectral graph invariants, and previously proposed graph positional encodings as special cases. Experiments show that our networks significantly outperform existing baselines on molecular graph regression, learning expressive graph representations, and learning neural fields on triangle meshes. Our code is available at https://github.com/cptq/SignNet-BasisNet . | ['Stefanie Jegelka', 'Haggai Maron', 'Suvrit Sra', 'Tess Smidt', 'Lingxiao Zhao', 'Joshua Robinson', 'Derek Lim'] | 2022-02-25 | null | null | null | null | ['graph-regression'] | ['graphs'] | [ 2.67608017e-01 1.74318776e-01 -5.55163026e-01 7.28368247e-03
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b59790a6-6118-455e-b0e3-60344316f5f5 | learning-accurate-performance-predictors-for | 2304.06393 | null | https://arxiv.org/abs/2304.06393v1 | https://arxiv.org/pdf/2304.06393v1.pdf | Learning Accurate Performance Predictors for Ultrafast Automated Model Compression | In this paper, we propose an ultrafast automated model compression framework called SeerNet for flexible network deployment. Conventional non-differen-tiable methods discretely search the desirable compression policy based on the accuracy from exhaustively trained lightweight models, and existing differentiable methods optimize an extremely large supernet to obtain the required compressed model for deployment. They both cause heavy computational cost due to the complex compression policy search and evaluation process. On the contrary, we obtain the optimal efficient networks by directly optimizing the compression policy with an accurate performance predictor, where the ultrafast automated model compression for various computational cost constraint is achieved without complex compression policy search and evaluation. Specifically, we first train the performance predictor based on the accuracy from uncertain compression policies actively selected by efficient evolutionary search, so that informative supervision is provided to learn the accurate performance predictor with acceptable cost. Then we leverage the gradient that maximizes the predicted performance under the barrier complexity constraint for ultrafast acquisition of the desirable compression policy, where adaptive update stepsizes with momentum are employed to enhance optimality of the acquired pruning and quantization strategy. Compared with the state-of-the-art automated model compression methods, experimental results on image classification and object detection show that our method achieves competitive accuracy-complexity trade-offs with significant reduction of the search cost. | ['Jie zhou', 'Shengyu Liu', 'Han Xiao', 'Jiwen Lu', 'Ziwei Wang'] | 2023-04-13 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 4.04816926e-01 2.55946219e-01 -7.06976831e-01 -4.17776972e-01
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6e820d0a-238f-4cf3-9193-318e4d57d8e6 | codex-a-comprehensive-knowledge-graph | 2009.07810 | null | https://arxiv.org/abs/2009.07810v2 | https://arxiv.org/pdf/2009.07810v2.pdf | CoDEx: A Comprehensive Knowledge Graph Completion Benchmark | We present CoDEx, a set of knowledge graph completion datasets extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty. In terms of scope, CoDEx comprises three knowledge graphs varying in size and structure, multilingual descriptions of entities and relations, and tens of thousands of hard negative triples that are plausible but verified to be false. To characterize CoDEx, we contribute thorough empirical analyses and benchmarking experiments. First, we analyze each CoDEx dataset in terms of logical relation patterns. Next, we report baseline link prediction and triple classification results on CoDEx for five extensively tuned embedding models. Finally, we differentiate CoDEx from the popular FB15K-237 knowledge graph completion dataset by showing that CoDEx covers more diverse and interpretable content, and is a more difficult link prediction benchmark. Data, code, and pretrained models are available at https://bit.ly/2EPbrJs. | ['Danai Koutra', 'Tara Safavi'] | 2020-09-16 | null | https://aclanthology.org/2020.emnlp-main.669 | https://aclanthology.org/2020.emnlp-main.669.pdf | emnlp-2020-11 | ['triple-classification'] | ['graphs'] | [-4.77153063e-01 5.60630620e-01 -7.29634583e-01 -1.56994984e-01
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8df98b1a-a955-4206-af55-064e03dd5033 | managing-multi-facet-bias-in-collaborative | 2302.10575 | null | https://arxiv.org/abs/2302.10575v1 | https://arxiv.org/pdf/2302.10575v1.pdf | Managing multi-facet bias in collaborative filtering recommender systems | Due to the extensive growth of information available online, recommender systems play a more significant role in serving people's interests. Traditional recommender systems mostly use an accuracy-focused approach to produce recommendations. Today's research suggests that this single-dimension approach can lead the system to be biased against a series of items with certain attributes. Biased recommendations across groups of items can endanger the interests of item providers along with causing user dissatisfaction with the system. This study aims to manage a new type of intersectional bias regarding the geographical origin and popularity of items in the output of state-of-the-art collaborative filtering recommender algorithms. We introduce an algorithm called MFAIR, a multi-facet post-processing bias mitigation algorithm to alleviate these biases. Extensive experiments on two real-world datasets of movies and books, enriched with the items' continents of production, show that the proposed algorithm strikes a reasonable balance between accuracy and both types of the mentioned biases. According to the results, our proposed approach outperforms a well-known competitor with no or only a slight loss of efficiency. | ['Saeed Farzi', 'Samira Vaez Barenji'] | 2023-02-21 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-4.12332565e-01 -2.27871805e-01 -3.14542919e-01 -5.69595993e-01
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f5d771fe-7786-4df1-8297-078737ea3ab0 | on-the-use-of-deep-learning-for-blind-image | 1602.05531 | null | http://arxiv.org/abs/1602.05531v5 | http://arxiv.org/pdf/1602.05531v5.pdf | On the Use of Deep Learning for Blind Image Quality Assessment | In this work we investigate the use of deep learning for distortion-generic
blind image quality assessment. We report on different design choices, ranging
from the use of features extracted from pre-trained Convolutional Neural
Networks (CNNs) as a generic image description, to the use of features
extracted from a CNN fine-tuned for the image quality task. Our best proposal,
named DeepBIQ, estimates the image quality by average pooling the scores
predicted on multiple sub-regions of the original image. The score of each
sub-region is computed using a Support Vector Regression (SVR) machine taking
as input features extracted using a CNN fine-tuned for category-based image
quality assessment. Experimental results on the LIVE In the Wild Image Quality
Challenge Database and on the LIVE Image Quality Assessment Database show that
DeepBIQ outperforms the state-of-the-art methods compared, having a Linear
Correlation Coefficient (LCC) with human subjective scores of almost 0.91 and
0.98 respectively. Furthermore, in most of the cases, the quality score
predictions of DeepBIQ are closer to the average observer than those of a
generic human observer. | ['Raimondo Schettini', 'Paolo Napoletano', 'Simone Bianco', 'Luigi Celona'] | 2016-02-17 | null | null | null | null | ['blind-image-quality-assessment'] | ['computer-vision'] | [-2.02161282e-01 -2.63836890e-01 6.05333485e-02 -5.65973103e-01
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b954fea5-c286-4fbd-b323-ea9016f9c6b7 | class-agnostic-counting | 1811.00472 | null | http://arxiv.org/abs/1811.00472v1 | http://arxiv.org/pdf/1811.00472v1.pdf | Class-Agnostic Counting | Nearly all existing counting methods are designed for a specific object
class. Our work, however, aims to create a counting model able to count any
class of object. To achieve this goal, we formulate counting as a matching
problem, enabling us to exploit the image self-similarity property that
naturally exists in object counting problems. We make the following three
contributions: first, a Generic Matching Network (GMN) architecture that can
potentially count any object in a class-agnostic manner; second, by
reformulating the counting problem as one of matching objects, we can take
advantage of the abundance of video data labeled for tracking, which contains
natural repetitions suitable for training a counting model. Such data enables
us to train the GMN. Third, to customize the GMN to different user
requirements, an adapter module is used to specialize the model with minimal
effort, i.e. using a few labeled examples, and adapting only a small fraction
of the trained parameters. This is a form of few-shot learning, which is
practical for domains where labels are limited due to requiring expert
knowledge (e.g. microbiology). We demonstrate the flexibility of our method on
a diverse set of existing counting benchmarks: specifically cells, cars, and
human crowds. The model achieves competitive performance on cell and crowd
counting datasets, and surpasses the state-of-the-art on the car dataset using
only three training images. When training on the entire dataset, the proposed
method outperforms all previous methods by a large margin. | ['Weidi Xie', 'Erika Lu', 'Andrew Zisserman'] | 2018-11-01 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 1.37898415e-01 -3.89450818e-01 -1.22617699e-01 -8.15828964e-02
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de625b38-f325-4849-b2e0-8505d565b645 | contextual-instance-decoupling-for-robust | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_Contextual_Instance_Decoupling_for_Robust_Multi-Person_Pose_Estimation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_Contextual_Instance_Decoupling_for_Robust_Multi-Person_Pose_Estimation_CVPR_2022_paper.pdf | Contextual Instance Decoupling for Robust Multi-Person Pose Estimation | Crowded scenes make it challenging to differentiate persons and locate their pose keypoints. This paper proposes the Contextual Instance Decoupling (CID), which presents a new pipeline for multi-person pose estimation. Instead of relying on person bounding boxes to spatially differentiate persons, CID decouples persons in an image into multiple instance-aware feature maps. Each of those feature maps is hence adopted to infer keypoints for a specific person. Compared with bounding box detection, CID is differentiable and robust to detection errors. Decoupling persons into different feature maps allows to isolate distractions from other persons, and explore context cues at scales larger than the bounding box size. Experiments show that CID outperforms previous multi-person pose estimation pipelines on crowded scenes pose estimation benchmarks in both accuracy and efficiency. For instance, it achieves 71.3% AP on CrowdPose, outperforming the recent single-stage DEKR by 5.6%, the bottom-up CenterAttention by 3.7%, and the top-down JC-SPPE by 5.3%. This advantage sustains on the commonly used COCO benchmark. | ['Shiliang Zhang', 'Dongkai Wang'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['multi-person-pose-estimation'] | ['computer-vision'] | [-3.07837218e-01 -2.19879314e-01 4.25589293e-01 -1.55755118e-01
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a91368ef-f0bd-4160-a208-97401e43b362 | croc-cross-view-online-clustering-for-dense | 2303.13245 | null | https://arxiv.org/abs/2303.13245v1 | https://arxiv.org/pdf/2303.13245v1.pdf | CrOC: Cross-View Online Clustering for Dense Visual Representation Learning | Learning dense visual representations without labels is an arduous task and more so from scene-centric data. We propose to tackle this challenging problem by proposing a Cross-view consistency objective with an Online Clustering mechanism (CrOC) to discover and segment the semantics of the views. In the absence of hand-crafted priors, the resulting method is more generalizable and does not require a cumbersome pre-processing step. More importantly, the clustering algorithm conjointly operates on the features of both views, thereby elegantly bypassing the issue of content not represented in both views and the ambiguous matching of objects from one crop to the other. We demonstrate excellent performance on linear and unsupervised segmentation transfer tasks on various datasets and similarly for video object segmentation. Our code and pre-trained models are publicly available at https://github.com/stegmuel/CrOC. | ['Jean-Philippe Thiran', 'Tinne Tuytelaars', 'Behzad Bozorgtabar', 'Tim Lebailly', 'Thomas Stegmüller'] | 2023-03-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Stegmuller_CrOC_Cross-View_Online_Clustering_for_Dense_Visual_Representation_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Stegmuller_CrOC_Cross-View_Online_Clustering_for_Dense_Visual_Representation_Learning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['online-clustering', 'unsupervised-semantic-segmentation', 'video-object-segmentation', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.10243016e-01 -1.05577216e-01 -3.00751012e-02 -4.15538996e-01
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8532ad73-ea73-429f-b4bc-ca7793f488e3 | pesco-prompt-enhanced-self-contrastive | 2305.14963 | null | https://arxiv.org/abs/2305.14963v1 | https://arxiv.org/pdf/2305.14963v1.pdf | PESCO: Prompt-enhanced Self Contrastive Learning for Zero-shot Text Classification | We present PESCO, a novel contrastive learning framework that substantially improves the performance of zero-shot text classification. We formulate text classification as a neural text matching problem where each document is treated as a query, and the system learns the mapping from each query to the relevant class labels by (1) adding prompts to enhance label matching, and (2) using retrieved labels to enrich the training set in a self-training loop of contrastive learning. PESCO achieves state-of-the-art performance on four benchmark text classification datasets. On DBpedia, we achieve 98.5\% accuracy without any labeled data, which is close to the fully-supervised result. Extensive experiments and analyses show all the components of PESCO are necessary for improving the performance of zero-shot text classification. | ['Yiming Yang', 'Ruohong Zhang', 'Ta-Chung Chi', 'Yau-Shian Wang'] | 2023-05-24 | null | null | null | null | ['text-matching'] | ['natural-language-processing'] | [ 2.72430152e-01 1.63868278e-01 -5.39336085e-01 -4.67030376e-01
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cc13d834-5868-4e0d-9dbf-5ae09d842393 | few-shot-text-classification-with-edge | null | null | https://aclanthology.org/2020.coling-main.485 | https://aclanthology.org/2020.coling-main.485.pdf | Few-Shot Text Classification with Edge-Labeling Graph Neural Network-Based Prototypical Network | In this paper, we propose a new few-shot text classification method. Compared with supervised learning methods which require a large corpus of labeled documents, our method aims to make it possible to classify unlabeled text with few labeled data. To achieve this goal, we take advantage of advanced pre-trained language model to extract the semantic features of each document. Furthermore, we utilize an edge-labeling graph neural network to implicitly models the intra-cluster similarity and the inter-cluster dissimilarity of the documents. Finally, we take the results of the graph neural network as the input of a prototypical network to classify the unlabeled texts. We verify the effectiveness of our method on a sentiment analysis dataset and a relation classification dataset and achieve the state-of-the-art performance on both tasks. | ['Ping Wang', 'Weijie Liu', 'Chen Lyu'] | 2020-12-01 | null | null | null | coling-2020-8 | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 1.01363458e-01 1.16249710e-01 -4.22824711e-01 -7.33353257e-01
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21dc113c-f987-426f-817c-3e646bdcff1a | relpose-recovering-6d-poses-from-sparse-view | 2305.04926 | null | https://arxiv.org/abs/2305.04926v1 | https://arxiv.org/pdf/2305.04926v1.pdf | RelPose++: Recovering 6D Poses from Sparse-view Observations | We address the task of estimating 6D camera poses from sparse-view image sets (2-8 images). This task is a vital pre-processing stage for nearly all contemporary (neural) reconstruction algorithms but remains challenging given sparse views, especially for objects with visual symmetries and texture-less surfaces. We build on the recent RelPose framework which learns a network that infers distributions over relative rotations over image pairs. We extend this approach in two key ways; first, we use attentional transformer layers to process multiple images jointly, since additional views of an object may resolve ambiguous symmetries in any given image pair (such as the handle of a mug that becomes visible in a third view). Second, we augment this network to also report camera translations by defining an appropriate coordinate system that decouples the ambiguity in rotation estimation from translation prediction. Our final system results in large improvements in 6D pose prediction over prior art on both seen and unseen object categories and also enables pose estimation and 3D reconstruction for in-the-wild objects. | ['Shubham Tulsiani', 'Deva Ramanan', 'Jason Y. Zhang', 'Amy Lin'] | 2023-05-08 | null | null | null | null | ['pose-prediction', '3d-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 3.02893907e-01 2.25105301e-01 -2.36352626e-02 -4.94716287e-01
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c4eb7dbb-ad20-407e-b345-ccc1a3157fef | does-the-geometry-of-word-embeddings-help-1 | 1705.10900 | null | http://arxiv.org/abs/1705.10900v1 | http://arxiv.org/pdf/1705.10900v1.pdf | Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology Based Representations | We investigate the pertinence of methods from algebraic topology for text
data analysis. These methods enable the development of
mathematically-principled isometric-invariant mappings from a set of vectors to
a document embedding, which is stable with respect to the geometry of the
document in the selected metric space. In this work, we evaluate the utility of
these topology-based document representations in traditional NLP tasks,
specifically document clustering and sentiment classification. We find that the
embeddings do not benefit text analysis. In fact, performance is worse than
simple techniques like $\textit{tf-idf}$, indicating that the geometry of the
document does not provide enough variability for classification on the basis of
topic or sentiment in the chosen datasets. | ['Paul Michel', 'Abhilasha Ravichander', 'Shruti Rijhwani'] | 2017-05-31 | null | null | null | null | ['document-embedding'] | ['methodology'] | [-2.78658479e-01 -8.50320980e-02 8.73555429e-03 -4.48256195e-01
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b15447cb-86be-4486-886e-2ee5b1e75006 | 3d-semantic-scene-completion-a-survey | 2103.07466 | null | https://arxiv.org/abs/2103.07466v3 | https://arxiv.org/pdf/2103.07466v3.pdf | 3D Semantic Scene Completion: a Survey | Semantic Scene Completion (SSC) aims to jointly estimate the complete geometry and semantics of a scene, assuming partial sparse input. In the last years following the multiplication of large-scale 3D datasets, SSC has gained significant momentum in the research community because it holds unresolved challenges. Specifically, SSC lies in the ambiguous completion of large unobserved areas and the weak supervision signal of the ground truth. This led to a substantially increasing number of papers on the matter. This survey aims to identify, compare and analyze the techniques providing a critical analysis of the SSC literature on both methods and datasets. Throughout the paper, we provide an in-depth analysis of the existing works covering all choices made by the authors while highlighting the remaining avenues of research. SSC performance of the SoA on the most popular datasets is also evaluated and analyzed. | ['Anne Verroust-Blondet', 'Raoul de Charette', 'Luis Roldao'] | 2021-03-12 | null | null | null | null | ['3d-semantic-scene-completion'] | ['computer-vision'] | [ 6.86318994e-01 5.27007103e-01 -1.72799807e-02 -3.61998558e-01
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c2dfca8c-b2b4-4821-afb7-6219e85a4ae6 | the-ties-that-matter-from-the-perspective-of | 2212.10960 | null | https://arxiv.org/abs/2212.10960v1 | https://arxiv.org/pdf/2212.10960v1.pdf | The Ties that matter: From the perspective of Similarity Measure in Online Social Networks | Online Social Networks have embarked on the importance of connection strength measures which has a broad array of applications such as, analyzing diffusion behaviors, community detection, link predictions, recommender systems. Though there are some existing connection strength measures, the density that a connection shares with it's neighbors and the directionality aspect has not received much attention. In this paper, we have proposed an asymmetric edge similarity measure namely, Neighborhood Density-based Edge Similarity (NDES) which provides a fundamental support to derive the strength of connection. The time complexity of NDES is $O(nk^2)$. An application of NDES for community detection in social network is shown. We have considered a similarity based community detection technique and substituted its similarity measure with NDES. The performance of NDES is evaluated on several small real-world datasets in terms of the effectiveness in detecting communities and compared with three widely used similarity measures. Empirical results show NDES enables detecting comparatively better communities both in terms of accuracy and quality. | ['Anupam Biswas', 'Soumita Das'] | 2022-12-21 | null | null | null | null | ['community-detection'] | ['graphs'] | [-2.26318553e-01 -1.74542651e-01 -1.28011376e-01 3.38896154e-03
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4b818f4a-b74d-405a-8430-accda697b363 | generative-prompt-tuning-for-relation | null | null | https://openreview.net/forum?id=lILSg7aInVH | https://openreview.net/pdf?id=lILSg7aInVH | Generative Prompt Tuning for Relation Classification | Prompt tuning is proposed to better tune pre-trained language models by filling the objective gap between the pre-training process and the downstream tasks. Current methods mainly convert the downstream tasks into masked language modeling (MLM) problems, which have proven effective for tasks with simple label sets. However, when applied to relation classification tasks which often exhibit a complex label space, vanilla prompt tuning methods designed for MLM may struggle with handling complex label verbalizations with variable length as in such methods, the locations and number of masked tokens are typically fixed. Inspired by the text infilling task for pre-training generative models that can flexibly predict missing spans, we propose a novel generative prompt tuning method to reformulate relation classification as an infilling problem to eliminate the rigid prompt restrictions, which allows our method to process label verbalizations of varying lengths at multiple predicted positions and thus be able to fully leverage rich semantics of entity and relation labels. In addition, we design entity-guided decoding and discriminative relation scoring to predict relations effectively and efficiently in the inference process. Extensive experiments under low-resource settings and fully supervised settings demonstrate the effectiveness of our approach. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['relation-classification', 'text-infilling'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.15768170e-01 5.69913745e-01 -5.42507529e-01 -6.90206528e-01
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1995f2a7-d30c-4ff7-bb8f-0372e5c8ab68 | dynamic-local-feature-aggregation-for | 2301.02836 | null | https://arxiv.org/abs/2301.02836v1 | https://arxiv.org/pdf/2301.02836v1.pdf | Dynamic Local Feature Aggregation for Learning on Point Clouds | Existing point cloud learning methods aggregate features from neighbouring points relying on constructing graph in the spatial domain, which results in feature update for each point based on spatially-fixed neighbours throughout layers. In this paper, we propose a dynamic feature aggregation (DFA) method that can transfer information by constructing local graphs in the feature domain without spatial constraints. By finding k-nearest neighbors in the feature domain, we perform relative position encoding and semantic feature encoding to explore latent position and feature similarity information, respectively, so that rich local features can be learned. At the same time, we also learn low-dimensional global features from the original point cloud for enhancing feature representation. Between DFA layers, we dynamically update the constructed local graph structure, so that we can learn richer information, which greatly improves adaptability and efficiency. We demonstrate the superiority of our method by conducting extensive experiments on point cloud classification and segmentation tasks. Implementation code is available: https://github.com/jiamang/DFA. | ['Ran Wei', 'Hui Yuan', 'Pan Gao', 'Zihao Li'] | 2023-01-07 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [-2.41566285e-01 -1.99925810e-01 -1.94124922e-01 -5.91400266e-01
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b939a05f-1081-4bb0-8d47-019ea34f05d0 | scatter-selective-context-attentional-scene | 2003.11288 | null | https://arxiv.org/abs/2003.11288v1 | https://arxiv.org/pdf/2003.11288v1.pdf | SCATTER: Selective Context Attentional Scene Text Recognizer | Scene Text Recognition (STR), the task of recognizing text against complex image backgrounds, is an active area of research. Current state-of-the-art (SOTA) methods still struggle to recognize text written in arbitrary shapes. In this paper, we introduce a novel architecture for STR, named Selective Context ATtentional Text Recognizer (SCATTER). SCATTER utilizes a stacked block architecture with intermediate supervision during training, that paves the way to successfully train a deep BiLSTM encoder, thus improving the encoding of contextual dependencies. Decoding is done using a two-step 1D attention mechanism. The first attention step re-weights visual features from a CNN backbone together with contextual features computed by a BiLSTM layer. The second attention step, similar to previous papers, treats the features as a sequence and attends to the intra-sequence relationships. Experiments show that the proposed approach surpasses SOTA performance on irregular text recognition benchmarks by 3.7\% on average. | ['Roee Litman', 'Ron Litman', 'Shahar Tsiper', 'Oron Anschel', 'Shai Mazor', 'R. Manmatha'] | 2020-03-25 | scatter-selective-context-attentional-scene-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Litman_SCATTER_Selective_Context_Attentional_Scene_Text_Recognizer_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Litman_SCATTER_Selective_Context_Attentional_Scene_Text_Recognizer_CVPR_2020_paper.pdf | cvpr-2020-6 | ['irregular-text-recognition'] | ['computer-vision'] | [ 1.0412002 -0.45914298 0.13979013 -0.4031875 -0.41113845 -0.16094016
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e191a96b-0a40-485f-8bf7-9fe453baed9b | confidence-aware-personalized-federated-1 | 2305.12557 | null | https://arxiv.org/abs/2305.12557v1 | https://arxiv.org/pdf/2305.12557v1.pdf | Confidence-aware Personalized Federated Learning via Variational Expectation Maximization | Federated Learning (FL) is a distributed learning scheme to train a shared model across clients. One common and fundamental challenge in FL is that the sets of data across clients could be non-identically distributed and have different sizes. Personalized Federated Learning (PFL) attempts to solve this challenge via locally adapted models. In this work, we present a novel framework for PFL based on hierarchical Bayesian modeling and variational inference. A global model is introduced as a latent variable to augment the joint distribution of clients' parameters and capture the common trends of different clients, optimization is derived based on the principle of maximizing the marginal likelihood and conducted using variational expectation maximization. Our algorithm gives rise to a closed-form estimation of a confidence value which comprises the uncertainty of clients' parameters and local model deviations from the global model. The confidence value is used to weigh clients' parameters in the aggregation stage and adjust the regularization effect of the global model. We evaluate our method through extensive empirical studies on multiple datasets. Experimental results show that our approach obtains competitive results under mild heterogeneous circumstances while significantly outperforming state-of-the-art PFL frameworks in highly heterogeneous settings. Our code is available at https://github.com/JunyiZhu-AI/confidence_aware_PFL. | ['Matthew B. Blaschko', 'Xingchen Ma', 'Junyi Zhu'] | 2023-05-21 | confidence-aware-personalized-federated | http://openaccess.thecvf.com//content/CVPR2023/html/Zhu_Confidence-Aware_Personalized_Federated_Learning_via_Variational_Expectation_Maximization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhu_Confidence-Aware_Personalized_Federated_Learning_via_Variational_Expectation_Maximization_CVPR_2023_paper.pdf | cvpr-2023-1 | ['personalized-federated-learning'] | ['methodology'] | [-5.35735607e-01 -2.97152460e-01 -2.99211204e-01 -6.08301282e-01
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a8fd5b5b-fca6-4e99-a6a1-43a87d413b7b | a-scope-sensitive-and-result-attentive-model | 2211.12220 | null | https://arxiv.org/abs/2211.12220v1 | https://arxiv.org/pdf/2211.12220v1.pdf | A Scope Sensitive and Result Attentive Model for Multi-Intent Spoken Language Understanding | Multi-Intent Spoken Language Understanding (SLU), a novel and more complex scenario of SLU, is attracting increasing attention. Unlike traditional SLU, each intent in this scenario has its specific scope. Semantic information outside the scope even hinders the prediction, which tremendously increases the difficulty of intent detection. More seriously, guiding slot filling with these inaccurate intent labels suffers error propagation problems, resulting in unsatisfied overall performance. To solve these challenges, in this paper, we propose a novel Scope-Sensitive Result Attention Network (SSRAN) based on Transformer, which contains a Scope Recognizer (SR) and a Result Attention Network (RAN). Scope Recognizer assignments scope information to each token, reducing the distraction of out-of-scope tokens. Result Attention Network effectively utilizes the bidirectional interaction between results of slot filling and intent detection, mitigating the error propagation problem. Experiments on two public datasets indicate that our model significantly improves SLU performance (5.4\% and 2.1\% on Overall accuracy) over the state-of-the-art baseline. | ['Weijia Jia', 'Wenmian Yang', 'Lizhi Cheng'] | 2022-11-22 | null | null | null | null | ['spoken-language-understanding', 'intent-detection', 'slot-filling', 'spoken-language-understanding'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'speech'] | [ 5.91924667e-01 1.71390459e-01 -2.30061516e-01 -3.82618755e-01
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8bd21ee7-e7f2-474e-88cb-45752b734727 | transfer-learning-for-personality-perception | 2305.16076 | null | https://arxiv.org/abs/2305.16076v2 | https://arxiv.org/pdf/2305.16076v2.pdf | Transfer Learning for Personality Perception via Speech Emotion Recognition | Holistic perception of affective attributes is an important human perceptual ability. However, this ability is far from being realized in current affective computing, as not all of the attributes are well studied and their interrelationships are poorly understood. In this work, we investigate the relationship between two affective attributes: personality and emotion, from a transfer learning perspective. Specifically, we transfer Transformer-based and wav2vec2-based emotion recognition models to perceive personality from speech across corpora. Compared with previous studies, our results show that transferring emotion recognition is effective for personality perception. Moreoever, this allows for better use and exploration of small personality corpora. We also provide novel findings on the relationship between personality and emotion that will aid future research on holistic affect recognition. | ['Catherine Lai', 'Peter Bell', 'Yuanchao Li'] | 2023-05-25 | null | null | null | null | ['speech-emotion-recognition'] | ['speech'] | [-1.75536070e-02 -6.27331734e-02 5.75659983e-02 -7.69957066e-01
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7e8fa824-1ed5-482a-a3cc-554170c792a5 | 190910393 | 1909.10393 | null | https://arxiv.org/abs/1909.10393v1 | https://arxiv.org/pdf/1909.10393v1.pdf | Specificity-Based Sentence Ordering for Multi-Document Extractive Risk Summarization | Risk mining technologies seek to find relevant textual extractions that capture entity-risk relationships. However, when high volume data sets are processed, a multitude of relevant extractions can be returned, shifting the focus to how best to present the results. We provide the details of a risk mining multi-document extractive summarization system that produces high quality output by modeling shifts in specificity that are characteristic of well-formed discourses. In particular, we propose a novel selection algorithm that alternates between extracts based on human curated or expanded autoencoded key terms, which exhibit greater specificity or generality as it relates to an entity-risk relationship. Through this extract ordering, and without the need for more complex discourse-aware NLP, we induce felicitous shifts in specificity in the alternating summaries that outperform non-alternating summaries on automatic ROUGE and BLEU scores, and manual understandability and preferences evaluations - achieving no statistically significant difference when compared to human authored summaries. | ['Eleanor Hagerman', 'Berk Ekmekci', 'Blake Howald'] | 2019-09-23 | null | null | null | null | ['sentence-ordering'] | ['natural-language-processing'] | [ 5.69726288e-01 7.44462013e-01 -4.58281457e-01 -1.38036907e-01
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fc7ff9d5-7eb3-4f9d-9e9c-b508bc56a51e | improving-user-controlled-table-to-text | 2302.09820 | null | https://arxiv.org/abs/2302.09820v1 | https://arxiv.org/pdf/2302.09820v1.pdf | Improving User Controlled Table-To-Text Generation Robustness | In this work we study user controlled table-to-text generation where users explore the content in a table by selecting cells and reading a natural language description thereof automatically produce by a natural language generator. Such generation models usually learn from carefully selected cell combinations (clean cell selections); however, in practice users may select unexpected, redundant, or incoherent cell combinations (noisy cell selections). In experiments, we find that models perform well on test sets coming from the same distribution as the train data but their performance drops when evaluated on realistic noisy user inputs. We propose a fine-tuning regime with additional user-simulated noisy cell selections. Models fine-tuned with the proposed regime gain 4.85 BLEU points on user noisy test cases and 1.4 on clean test cases; and achieve comparable state-of-the-art performance on the ToTTo dataset. | ['Laura Perez-Beltrachini', 'Zhongyi Yu', 'Yunqing Liu', 'Hanxu Hu'] | 2023-02-20 | null | null | null | null | ['table-to-text-generation'] | ['natural-language-processing'] | [ 3.54670435e-01 2.52906352e-01 -3.64845902e-01 -5.73635567e-03
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8c753ca2-1382-4efb-af51-19cca961bfcc | combining-task-and-dialogue-streams-in | null | null | https://aclanthology.org/W14-4316 | https://aclanthology.org/W14-4316.pdf | Combining Task and Dialogue Streams in Unsupervised Dialogue Act Models | null | ['Aysu Ezen-Can', 'Kristy Boyer'] | 2014-06-01 | null | null | null | ws-2014-6 | ['dialogue-act-classification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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cdd6edf4-5fef-44d8-8dfc-9067a754e47a | decomposition-compression-and-synthesis-dcs | 2012.00650 | null | https://arxiv.org/abs/2012.00650v4 | https://arxiv.org/pdf/2012.00650v4.pdf | Decoder-side Cross Resolution Synthesis for Video Compression Enhancement | This paper proposes a decoder-side Cross Resolution Synthesis (CRS) module to pursue better compression efficiency beyond the latest Versatile Video Coding (VVC), where we encode intra frames at original high resolution (HR), compress inter frames at a lower resolution (LR), and then super-resolve decoded LR inter frames with the help from preceding HR intra and neighboring LR inter frames. For a LR inter frame, a motion alignment and aggregation network (MAN) is devised to produce temporally aggregated motion representation to best guarantee the temporal smoothness; Another texture compensation network (TCN) is utilized to generate texture representation from decoded HR intra frame for better augmenting spatial details; Finally, a similarity-driven fusion engine synthesizes motion and texture representations to upscale LR inter frames for the removal of compression and resolution re-sampling noises. We enhance the VVC using proposed CRS, showing averaged 8.76% and 11.93% Bj{\o}ntegaard Delta Rate (BD-Rate) gains against the latest VVC anchor in Random Access (RA) and Low-delay P (LDP) settings respectively. In addition, experimental comparisons to the state-of-the-art super-resolution (SR) based VVC enhancement methods, and ablation studies are conducted to further report superior efficiency and generalization of the proposed algorithm. All materials will be made to public at https://njuvision.github.io/CRS for reproducible research. | ['zhenyu Dai', 'Dandan Ding', 'Dong Wang', 'Zhan Ma', 'Tong Chen', 'Ming Lu'] | 2020-12-01 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 6.32027805e-01 -1.81603074e-01 -3.51871789e-01 -8.29556286e-02
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45984123-c886-48a8-9c38-1faa33dcf569 | option-tracing-beyond-correctness-analysis-in | 2104.09043 | null | https://arxiv.org/abs/2104.09043v1 | https://arxiv.org/pdf/2104.09043v1.pdf | Option Tracing: Beyond Correctness Analysis in Knowledge Tracing | Knowledge tracing refers to a family of methods that estimate each student's knowledge component/skill mastery level from their past responses to questions. One key limitation of most existing knowledge tracing methods is that they can only estimate an \emph{overall} knowledge level of a student per knowledge component/skill since they analyze only the (usually binary-valued) correctness of student responses. Therefore, it is hard to use them to diagnose specific student errors. In this paper, we extend existing knowledge tracing methods beyond correctness prediction to the task of predicting the exact option students select in multiple choice questions. We quantitatively evaluate the performance of our option tracing methods on two large-scale student response datasets. We also qualitatively evaluate their ability in identifying common student errors in the form of clusters of incorrect options across different questions that correspond to the same error. | ['Andrew Lan', 'Jay Raspat', 'Aritra Ghosh'] | 2021-04-19 | null | null | null | null | ['skill-mastery'] | ['robots'] | [-1.35528475e-01 -1.21207252e-01 -2.62048095e-01 -5.63508272e-01
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6b584eb9-9f16-4569-8200-e88868203a30 | traj-mae-masked-autoencoders-for-trajectory | 2303.06697 | null | https://arxiv.org/abs/2303.06697v1 | https://arxiv.org/pdf/2303.06697v1.pdf | Traj-MAE: Masked Autoencoders for Trajectory Prediction | Trajectory prediction has been a crucial task in building a reliable autonomous driving system by anticipating possible dangers. One key issue is to generate consistent trajectory predictions without colliding. To overcome the challenge, we propose an efficient masked autoencoder for trajectory prediction (Traj-MAE) that better represents the complicated behaviors of agents in the driving environment. Specifically, our Traj-MAE employs diverse masking strategies to pre-train the trajectory encoder and map encoder, allowing for the capture of social and temporal information among agents while leveraging the effect of environment from multiple granularities. To address the catastrophic forgetting problem that arises when pre-training the network with multiple masking strategies, we introduce a continual pre-training framework, which can help Traj-MAE learn valuable and diverse information from various strategies efficiently. Our experimental results in both multi-agent and single-agent settings demonstrate that Traj-MAE achieves competitive results with state-of-the-art methods and significantly outperforms our baseline model. | ['Pheng-Ann Heng', 'Guangyong Chen', 'Chenyong Guan', 'Jianye Hao', 'Furui Liu', 'Kun Shao', 'Jiaze Wang', 'Hao Chen'] | 2023-03-12 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [-3.02722812e-01 -1.82578430e-01 -6.93493560e-02 -2.83370316e-01
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86042f48-9761-4a45-8d4a-54276a6fe53a | noisy-deductive-reasoning-how-humans | 2012.08298 | null | https://arxiv.org/abs/2012.08298v1 | https://arxiv.org/pdf/2012.08298v1.pdf | Noisy Deductive Reasoning: How Humans Construct Math, and How Math Constructs Universes | We present a computational model of mathematical reasoning according to which mathematics is a fundamentally stochastic process. That is, on our model, whether or not a given formula is deemed a theorem in some axiomatic system is not a matter of certainty, but is instead governed by a probability distribution. We then show that this framework gives a compelling account of several aspects of mathematical practice. These include: 1) the way in which mathematicians generate research programs, 2) the applicability of Bayesian models of mathematical heuristics, 3) the role of abductive reasoning in mathematics, 4) the way in which multiple proofs of a proposition can strengthen our degree of belief in that proposition, and 5) the nature of the hypothesis that there are multiple formal systems that are isomorphic to physically possible universes. Thus, by embracing a model of mathematics as not perfectly predictable, we generate a new and fruitful perspective on the epistemology and practice of mathematics. | ['David Kinney', 'David H. Wolpert'] | 2020-10-28 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 1.06204741e-01 4.98784184e-01 8.93203169e-02 -7.28736743e-02
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967bc7bf-a0d6-4a01-9c80-98d1563ad7e3 | see-and-think-disentangling-semantic-scene | null | null | http://papers.nips.cc/paper/7310-see-and-think-disentangling-semantic-scene-completion | http://papers.nips.cc/paper/7310-see-and-think-disentangling-semantic-scene-completion.pdf | See and Think: Disentangling Semantic Scene Completion | Semantic scene completion predicts volumetric occupancy and object category of a 3D scene, which helps intelligent agents to understand and interact with the surroundings. In this work, we propose a disentangled framework, sequentially carrying out 2D semantic segmentation, 2D-3D reprojection and 3D semantic scene completion. This three-stage framework has three advantages: (1) explicit semantic segmentation significantly boosts performance; (2) flexible fusion ways of sensor data bring good extensibility; (3) progress in any subtask will promote the holistic performance. Experimental results show that regardless of inputing a single depth or RGB-D, our framework can generate high-quality semantic scene completion, and outperforms state-of-the-art approaches on both synthetic and real datasets. | ['Xiaowei Li', 'Yinhe Han', 'Shice Liu', 'Yu Hu', 'Yiming Zeng', 'Qiankun Tang', 'Beibei Jin'] | 2018-12-01 | null | null | null | neurips-2018-12 | ['3d-semantic-scene-completion', '2d-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.01982635e-01 1.94936320e-01 1.35374372e-03 -4.50295478e-01
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7b7804cc-52fc-4157-a2e7-121d6811eba3 | icnet-for-real-time-semantic-segmentation-on | 1704.08545 | null | http://arxiv.org/abs/1704.08545v2 | http://arxiv.org/pdf/1704.08545v2.pdf | ICNet for Real-Time Semantic Segmentation on High-Resolution Images | We focus on the challenging task of real-time semantic segmentation in this
paper. It finds many practical applications and yet is with fundamental
difficulty of reducing a large portion of computation for pixel-wise label
inference. We propose an image cascade network (ICNet) that incorporates
multi-resolution branches under proper label guidance to address this
challenge. We provide in-depth analysis of our framework and introduce the
cascade feature fusion unit to quickly achieve high-quality segmentation. Our
system yields real-time inference on a single GPU card with decent quality
results evaluated on challenging datasets like Cityscapes, CamVid and
COCO-Stuff. | ['Xiaoyong Shen', 'Xiaojuan Qi', 'Hengshuang Zhao', 'Jiaya Jia', 'Jianping Shi'] | 2017-04-27 | icnet-for-real-time-semantic-segmentation-on-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Hengshuang_Zhao_ICNet_for_Real-Time_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_ICNet_for_Real-Time_ECCV_2018_paper.pdf | eccv-2018-9 | ['thermal-image-segmentation', 'dichotomous-image-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.98948133e-01 -1.43125623e-01 -7.25409836e-02 -4.59798217e-01
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fe83f326-1f2f-4e99-bac4-dca245cbcf46 | bulbar-als-detection-based-on-analysis-of | 2003.10806 | null | https://arxiv.org/abs/2003.10806v1 | https://arxiv.org/pdf/2003.10806v1.pdf | Bulbar ALS Detection Based on Analysis of Voice Perturbation and Vibrato | On average the lack of biological markers causes a one year diagnostic delay to detect amyotrophic lateral sclerosis (ALS). To improve the diagnostic process an automatic voice assessment based on acoustic analysis can be used. The purpose of this work was to verify the sutability of the sustain vowel phonation test for automatic detection of patients with ALS. We proposed enhanced procedure for separation of voice signal into fundamental periods that requires for calculation of perturbation measurements (such as jitter and shimmer). Also we proposed method for quantitative assessment of pathological vibrato manifestations in sustain vowel phonation. The study's experiments show that using the proposed acoustic analysis methods, the classifier based on linear discriminant analysis attains 90.7\% accuracy with 86.7\% sensitivity and 92.2\% specificity. | ['Alexander Petrovsky', 'Maxim Vashkevich', 'Yuliya Rushkevich'] | 2020-03-24 | null | null | null | null | ['als-detection'] | ['medical'] | [ 2.17964262e-01 -2.93164521e-01 2.33529657e-01 -1.14588171e-01
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f55f66e7-bee9-42ad-ae08-f42de568f5f0 | edface-celeb-1m-benchmarking-face | 2110.05031 | null | https://arxiv.org/abs/2110.05031v2 | https://arxiv.org/pdf/2110.05031v2.pdf | EDFace-Celeb-1M: Benchmarking Face Hallucination with a Million-scale Dataset | Recent deep face hallucination methods show stunning performance in super-resolving severely degraded facial images, even surpassing human ability. However, these algorithms are mainly evaluated on non-public synthetic datasets. It is thus unclear how these algorithms perform on public face hallucination datasets. Meanwhile, most of the existing datasets do not well consider the distribution of races, which makes face hallucination methods trained on these datasets biased toward some specific races. To address the above two problems, in this paper, we build a public Ethnically Diverse Face dataset, EDFace-Celeb-1M, and design a benchmark task for face hallucination. Our dataset includes 1.7 million photos that cover different countries, with balanced race composition. To the best of our knowledge, it is the largest and publicly available face hallucination dataset in the wild. Associated with this dataset, this paper also contributes various evaluation protocols and provides comprehensive analysis to benchmark the existing state-of-the-art methods. The benchmark evaluations demonstrate the performance and limitations of state-of-the-art algorithms. | ['Stefanos Zafeiriou', 'Wei Liu', 'Jiankang Deng', 'Jingyu Liu', 'Wenhan Luo', 'Dongxu Li', 'Kaihao Zhang'] | 2021-10-11 | null | null | null | null | ['face-hallucination'] | ['computer-vision'] | [-9.09238905e-02 7.01808482e-02 -1.09724514e-01 -3.41348767e-01
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022030cb-0401-474c-9c70-f75f1ad0f835 | what-makes-for-good-views-for-contrastive | 2005.10243 | null | https://arxiv.org/abs/2005.10243v3 | https://arxiv.org/pdf/2005.10243v3.pdf | What Makes for Good Views for Contrastive Learning? | Contrastive learning between multiple views of the data has recently achieved state of the art performance in the field of self-supervised representation learning. Despite its success, the influence of different view choices has been less studied. In this paper, we use theoretical and empirical analysis to better understand the importance of view selection, and argue that we should reduce the mutual information (MI) between views while keeping task-relevant information intact. To verify this hypothesis, we devise unsupervised and semi-supervised frameworks that learn effective views by aiming to reduce their MI. We also consider data augmentation as a way to reduce MI, and show that increasing data augmentation indeed leads to decreasing MI and improves downstream classification accuracy. As a by-product, we achieve a new state-of-the-art accuracy on unsupervised pre-training for ImageNet classification ($73\%$ top-1 linear readout with a ResNet-50). In addition, transferring our models to PASCAL VOC object detection and COCO instance segmentation consistently outperforms supervised pre-training. Code:http://github.com/HobbitLong/PyContrast | ['Cordelia Schmid', 'Chen Sun', 'Ben Poole', 'Yonglong Tian', 'Phillip Isola', 'Dilip Krishnan'] | 2020-05-20 | what-makes-for-good-views-for-contrastive-1 | http://proceedings.neurips.cc/paper/2020/hash/4c2e5eaae9152079b9e95845750bb9ab-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/4c2e5eaae9152079b9e95845750bb9ab-Paper.pdf | neurips-2020-12 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 3.12384725e-01 1.85416773e-01 -3.56754512e-01 -5.31649828e-01
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1900a4e9-7743-446b-9fd4-22ca0621c76b | bi-level-dynamic-learning-for-jointly-multi | 2305.06720 | null | https://arxiv.org/abs/2305.06720v1 | https://arxiv.org/pdf/2305.06720v1.pdf | Bi-level Dynamic Learning for Jointly Multi-modality Image Fusion and Beyond | Recently, multi-modality scene perception tasks, e.g., image fusion and scene understanding, have attracted widespread attention for intelligent vision systems. However, early efforts always consider boosting a single task unilaterally and neglecting others, seldom investigating their underlying connections for joint promotion. To overcome these limitations, we establish the hierarchical dual tasks-driven deep model to bridge these tasks. Concretely, we firstly construct an image fusion module to fuse complementary characteristics and cascade dual task-related modules, including a discriminator for visual effects and a semantic network for feature measurement. We provide a bi-level perspective to formulate image fusion and follow-up downstream tasks. To incorporate distinct task-related responses for image fusion, we consider image fusion as a primary goal and dual modules as learnable constraints. Furthermore, we develop an efficient first-order approximation to compute corresponding gradients and present dynamic weighted aggregation to balance the gradients for fusion learning. Extensive experiments demonstrate the superiority of our method, which not only produces visually pleasant fused results but also realizes significant promotion for detection and segmentation than the state-of-the-art approaches. | ['Risheng Liu', 'Xin Fan', 'Long Ma', 'Guanyao Wu', 'JinYuan Liu', 'Zhu Liu'] | 2023-05-11 | null | null | null | null | ['scene-understanding'] | ['computer-vision'] | [ 4.82877195e-01 -2.53130376e-01 1.07790582e-01 -5.29586911e-01
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8bccb532-155a-44fd-ad6d-69b7f2fbf565 | bias-mitigation-techniques-in-image | 2303.11449 | null | https://arxiv.org/abs/2303.11449v1 | https://arxiv.org/pdf/2303.11449v1.pdf | Bias mitigation techniques in image classification: fair machine learning in human heritage collections | A major problem with using automated classification systems is that if they are not engineered correctly and with fairness considerations, they could be detrimental to certain populations. Furthermore, while engineers have developed cutting-edge technologies for image classification, there is still a gap in the application of these models in human heritage collections, where data sets usually consist of low-quality pictures of people with diverse ethnicity, gender, and age. In this work, we evaluate three bias mitigation techniques using two state-of-the-art neural networks, Xception and EfficientNet, for gender classification. Moreover, we explore the use of transfer learning using a fair data set to overcome the training data scarcity. We evaluated the effectiveness of the bias mitigation pipeline on a cultural heritage collection of photographs from the 19th and 20th centuries, and we used the FairFace data set for the transfer learning experiments. After the evaluation, we found that transfer learning is a good technique that allows better performance when working with a small data set. Moreover, the fairest classifier was found to be accomplished using transfer learning, threshold change, re-weighting and image augmentation as bias mitigation methods. | ['Christoph Nötzli', 'Erik Norén', 'Sushruth Badri', 'Dalia Ortiz Pablo'] | 2023-03-20 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 9.53092277e-02 1.76709667e-01 -1.92914065e-02 -6.45370841e-01
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0740a040-4238-44ae-8043-1dcfd10b07ea | adaptive-course-recommendation-system | null | null | https://doi.org/10.1016/j.knosys.2021.107085 | https://drive.google.com/file/d/14hHF9YQ_PlMGtzE6jk4qOryLMmO_tLeQ/view?usp=sharing | Adaptive Course Recommendation System | In the process of course learning, users incline to change their interests with the improvements of their cognition. Existing course recommendation methods usually assume that users’ preferences are static. They fail to capture the user’s dynamic interests in sequential learning behaviours. In this respect, the recommendations show low accuracy and adaptivity, especially when users have diverse interests in many different courses. Thus, they may not be suitable for applying in the online course recommendation scenario. In this paper, we propose a novel course recommendation framework, named Dynamic Attention and hierarchical Reinforcement Learning (DARL), to improve the adaptivity of the recommendation model. DARL automatically captures the user’s preferences in each interaction between a profile reviser and a recommendation model, and thereby enhances the effectiveness of course recommendation. For tracking the changes in users’ preferences, DARL adaptively updates the attention weight of the corresponding course at different sessions to improve the recommendation accuracy. We perform empirical experiments on two real-world MOOCs (i.e., Massive Open Online Courses) datasets. Experimental results demonstrate that DARL significantly outperforms state-of-the-art course recommendation methods in terms of major evaluation metrics. | ['Pengcheng Wu', 'Yong liu', 'Wenhua Zeng', 'Fan Lin', 'Shibo Feng', 'Yuanguo Lin'] | 2021-07-19 | null | null | null | journal-2021-7 | ['hierarchical-reinforcement-learning'] | ['methodology'] | [-1.16885379e-01 -3.79879355e-01 -4.65740055e-01 -3.67818713e-01
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f82ab540-8076-4192-adc4-0e7d0399fa2a | alleviation-of-temperature-variation-induced | 2103.03111 | null | https://arxiv.org/abs/2103.03111v5 | https://arxiv.org/pdf/2103.03111v5.pdf | Alleviation of Temperature Variation Induced Accuracy Degradation in Ferroelectric FinFET Based Neural Network | This paper reports the impacts of temperature variation on the inference accuracy of pre-trained all-ferroelectric FinFET deep neural networks, along with plausible design techniques to abate these impacts. We adopted a pre-trained artificial neural network (N.N.) with 96.4% inference accuracy on the MNIST dataset as the baseline. As an aftermath of temperature change, a compact model captured the conductance drift of a programmed cell over a wide range of gate biases. We observed a significant inference accuracy degradation in the analog neural network at 233 K for an N.N. trained at 300 K. Finally, we deployed binary neural networks with "read voltage" optimization to ensure immunity of N.N. to accuracy degradation under temperature variation, maintaining an inference accuracy of 96%. Keywords: Ferroelectric memories | ['Thomas Kämpfe', 'Darsen D. Lu', 'Md. Aftab Baig', 'Hoang-Hiep Le', 'Yao-Jen Lee', 'Sourav De'] | 2021-03-03 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 4.66637999e-01 -1.04049683e-01 4.60682921e-02 -5.97620308e-01
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c0285b74-ade8-4349-bbed-e284658270dc | visual-prompting-for-adversarial-robustness | 2210.06284 | null | https://arxiv.org/abs/2210.06284v4 | https://arxiv.org/pdf/2210.06284v4.pdf | Visual Prompting for Adversarial Robustness | In this work, we leverage visual prompting (VP) to improve adversarial robustness of a fixed, pre-trained model at testing time. Compared to conventional adversarial defenses, VP allows us to design universal (i.e., data-agnostic) input prompting templates, which have plug-and-play capabilities at testing time to achieve desired model performance without introducing much computation overhead. Although VP has been successfully applied to improving model generalization, it remains elusive whether and how it can be used to defend against adversarial attacks. We investigate this problem and show that the vanilla VP approach is not effective in adversarial defense since a universal input prompt lacks the capacity for robust learning against sample-specific adversarial perturbations. To circumvent it, we propose a new VP method, termed Class-wise Adversarial Visual Prompting (C-AVP), to generate class-wise visual prompts so as to not only leverage the strengths of ensemble prompts but also optimize their interrelations to improve model robustness. Our experiments show that C-AVP outperforms the conventional VP method, with 2.1X standard accuracy gain and 2X robust accuracy gain. Compared to classical test-time defenses, C-AVP also yields a 42X inference time speedup. | ['Sijia Liu', 'Pin-Yu Chen', 'Yuguang Yao', 'Peter Lorenz', 'Aochuan Chen'] | 2022-10-12 | null | null | null | null | ['adversarial-defense', 'visual-prompting'] | ['adversarial', 'computer-vision'] | [ 1.70193866e-01 -2.43287310e-01 -7.46950135e-02 -8.36358294e-02
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4c5230b8-9f87-4cff-9d0b-881183bf8645 | sparse-modular-activation-for-efficient | 2306.11197 | null | https://arxiv.org/abs/2306.11197v2 | https://arxiv.org/pdf/2306.11197v2.pdf | Sparse Modular Activation for Efficient Sequence Modeling | Linear State Space Models (SSMs) have demonstrated strong performance in a variety of sequence modeling tasks due to their efficient encoding of the recurrent structure. However, in more comprehensive tasks like language modeling and machine translation, self-attention-based models still outperform SSMs. Hybrid models employing both SSM and self-attention generally show promising performance, but current approaches apply attention modules statically and uniformly to all elements in the input sequences, leading to sub-optimal quality-efficiency trade-offs. In this work, we introduce Sparse Modular Activation (SMA), a general mechanism enabling neural networks to sparsely and dynamically activate sub-modules for sequence elements in a differentiable manner. Through allowing each element to skip non-activated sub-modules, SMA reduces computation and memory consumption at both training and inference stages of sequence modeling. As a specific instantiation of SMA, we design a novel neural architecture, SeqBoat, which employs SMA to sparsely activate a Gated Attention Unit (GAU) based on the state representations learned from an SSM. By constraining the GAU to only conduct local attention on the activated inputs, SeqBoat can achieve linear inference complexity with theoretically infinite attention span, and provide substantially better quality-efficiency trade-off than the chunking-based models. With experiments on a wide range of tasks, including language modeling, speech classification and long-range arena, SeqBoat brings new state-of-the-art results among hybrid models with linear complexity and reveals the amount of attention needed for each task through the learned sparse activation patterns. | ['ChengXiang Zhai', 'Chenguang Zhu', 'Yichong Xu', 'Shuohang Wang', 'Yang Liu', 'Liliang Ren'] | 2023-06-19 | null | null | null | null | ['machine-translation', 'chunking'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.66833001e-01 2.12365463e-01 -6.49868131e-01 -2.35836789e-01
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d8069253-36ee-4a89-9653-7ab340f06b91 | a-simple-yet-effective-baseline-for-3d-human | 1705.03098 | null | http://arxiv.org/abs/1705.03098v2 | http://arxiv.org/pdf/1705.03098v2.pdf | A simple yet effective baseline for 3d human pose estimation | Following the success of deep convolutional networks, state-of-the-art
methods for 3d human pose estimation have focused on deep end-to-end systems
that predict 3d joint locations given raw image pixels. Despite their excellent
performance, it is often not easy to understand whether their remaining error
stems from a limited 2d pose (visual) understanding, or from a failure to map
2d poses into 3-dimensional positions. With the goal of understanding these
sources of error, we set out to build a system that given 2d joint locations
predicts 3d positions. Much to our surprise, we have found that, with current
technology, "lifting" ground truth 2d joint locations to 3d space is a task
that can be solved with a remarkably low error rate: a relatively simple deep
feed-forward network outperforms the best reported result by about 30\% on
Human3.6M, the largest publicly available 3d pose estimation benchmark.
Furthermore, training our system on the output of an off-the-shelf
state-of-the-art 2d detector (\ie, using images as input) yields state of the
art results -- this includes an array of systems that have been trained
end-to-end specifically for this task. Our results indicate that a large
portion of the error of modern deep 3d pose estimation systems stems from their
visual analysis, and suggests directions to further advance the state of the
art in 3d human pose estimation. | ['James J. Little', 'Rayat Hossain', 'Javier Romero', 'Julieta Martinez'] | 2017-05-08 | a-simple-yet-effective-baseline-for-3d-human-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Martinez_A_Simple_yet_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Martinez_A_Simple_yet_ICCV_2017_paper.pdf | iccv-2017-10 | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [-2.09278435e-01 1.64187819e-01 -1.73201058e-02 -4.21299756e-01
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1b458446-f0be-47de-8d9a-83422f0097de | convolutional-ensembling-based-few-shot | 2208.03288 | null | https://arxiv.org/abs/2208.03288v3 | https://arxiv.org/pdf/2208.03288v3.pdf | Convolutional Ensembling based Few-Shot Defect Detection Technique | Over the past few years, there has been a significant improvement in the domain of few-shot learning. This learning paradigm has shown promising results for the challenging problem of anomaly detection, where the general task is to deal with heavy class imbalance. Our paper presents a new approach to few-shot classification, where we employ the knowledge-base of multiple pre-trained convolutional models that act as the backbone for our proposed few-shot framework. Our framework uses a novel ensembling technique for boosting the accuracy while drastically decreasing the total parameter count, thus paving the way for real-time implementation. We perform an extensive hyperparameter search using a power-line defect detection dataset and obtain an accuracy of 92.30% for the 5-way 5-shot task. Without further tuning, we evaluate our model on competing standards with the existing state-of-the-art methods and outperform them. | ['Sumeet Saurav', 'Prashant Sadashiv Gidde', 'Sanjay Singh', 'Abeer Banerjee', 'Soumyajit Karmakar'] | 2022-08-05 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 3.51055153e-02 -1.99074730e-01 -1.67168006e-01 -3.83939445e-01
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a0cc4816-e8f8-4cd7-9000-4a6c4b134c40 | prediction-of-oral-food-challenges-via | 2208.08268 | null | https://arxiv.org/abs/2208.08268v2 | https://arxiv.org/pdf/2208.08268v2.pdf | Prediction of Oral Food Challenge Outcomes via Ensemble Learning | Oral Food Challenges (OFCs) are essential to accurately diagnosing food allergy due to the limitations of existing clinical testing. However, some patients are hesitant to undergo OFCs, while those willing suffer from limited access to allergists in rural/community healthcare settings. Despite its success in predicting patient outcomes in other clinical settings, few applications of machine learning to food allergy have been developed. Thus, in this study, we seek to leverage machine learning methodologies for OFC outcome prediction. Retrospective data was gathered from 1,112 patients who collectively underwent a total of 1,284 OFCs, and consisted of clinical factors including serum-specific Immunoglobulin E (IgE), total IgE, skin prick tests (SPTs), comorbidities, sex, and age. Using these features, multiple machine learning models were constructed to predict OFC outcomes for three common allergens: peanut, egg, and milk. The best performing model for each allergen was an ensemble of random forest (egg) or Learning Using Concave and Convex Kernels (LUCCK) (peanut, milk) models, which achieved an Area under the Curve (AUC) of 0.91, 0.96, and 0.94, in predicting OFC outcomes for peanut, egg, and milk, respectively. Moreover, all such models had sensitivity and specificity values 89%. Model interpretation via SHapley Additive exPlanations (SHAP) indicates that specific IgE, along with wheal and flare values from SPTs, are highly predictive of OFC outcomes. The results of this analysis suggest that ensemble learning has the potential to predict OFC outcomes and reveal relevant clinical factors for further study. | ['Jonathan Gryak', 'Georgiana Sanders', 'Rajan Ravikumar', 'Kayvan Najarian', 'Diane Shaltis', 'Kylie Jungles', 'Deborah Lee', 'Justin Zhang'] | 2022-08-17 | null | null | null | null | ['predicting-patient-outcomes'] | ['medical'] | [ 4.61120605e-02 -4.13649589e-01 -4.84580278e-01 -3.35065484e-01
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6288ec01-9c17-4714-9374-3b052133adbd | learning-by-novel-view-synthesis-for-full | 2201.07927 | null | https://arxiv.org/abs/2201.07927v3 | https://arxiv.org/pdf/2201.07927v3.pdf | Learning-by-Novel-View-Synthesis for Full-Face Appearance-Based 3D Gaze Estimation | Despite recent advances in appearance-based gaze estimation techniques, the need for training data that covers the target head pose and gaze distribution remains a crucial challenge for practical deployment. This work examines a novel approach for synthesizing gaze estimation training data based on monocular 3D face reconstruction. Unlike prior works using multi-view reconstruction, photo-realistic CG models, or generative neural networks, our approach can manipulate and extend the head pose range of existing training data without any additional requirements. We introduce a projective matching procedure to align the reconstructed 3D facial mesh with the camera coordinate system and synthesize face images with accurate gaze labels. We also propose a mask-guided gaze estimation model and data augmentation strategies to further improve the estimation accuracy by taking advantage of synthetic training data. Experiments using multiple public datasets show that our approach significantly improves the estimation performance on challenging cross-dataset settings with non-overlapping gaze distributions. | ['Yusuke Sugano', 'Takuru Shimoyama', 'Jiawei Qin'] | 2022-01-20 | null | null | null | null | ['3d-face-reconstruction', 'gaze-estimation', 'face-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.13563263e-01 1.27918318e-01 -2.81098615e-02 -7.70941734e-01
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bbacc723-f1ee-4fc5-84a8-b5e3883f29bb | phd-gifs-personalized-highlight-detection-for | 1804.06604 | null | http://arxiv.org/abs/1804.06604v2 | http://arxiv.org/pdf/1804.06604v2.pdf | PHD-GIFs: Personalized Highlight Detection for Automatic GIF Creation | Highlight detection models are typically trained to identify cues that make
visual content appealing or interesting for the general public, with the
objective of reducing a video to such moments. However, the "interestingness"
of a video segment or image is subjective. Thus, such highlight models provide
results of limited relevance for the individual user. On the other hand,
training one model per user is inefficient and requires large amounts of
personal information which is typically not available. To overcome these
limitations, we present a global ranking model which conditions on each
particular user's interests. Rather than training one model per user, our model
is personalized via its inputs, which allows it to effectively adapt its
predictions, given only a few user-specific examples. To train this model, we
create a large-scale dataset of users and the GIFs they created, giving us an
accurate indication of their interests. Our experiments show that using the
user history substantially improves the prediction accuracy. On our test set of
850 videos, our model improves the recall by 8% with respect to generic
highlight detectors. Furthermore, our method proves more precise than the
user-agnostic baselines even with just one person-specific example. | ['Ana García del Molino', 'Michael Gygli'] | 2018-04-18 | null | null | null | null | ['highlight-detection'] | ['computer-vision'] | [ 1.77625995e-02 -2.35335022e-01 -5.22832811e-01 -4.10286427e-01
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3e716d93-e7a3-4159-9cc4-bb88a58e349d | elixirnet-relation-aware-network-architecture | 2003.08770 | null | https://arxiv.org/abs/2003.08770v1 | https://arxiv.org/pdf/2003.08770v1.pdf | ElixirNet: Relation-aware Network Architecture Adaptation for Medical Lesion Detection | Most advances in medical lesion detection network are limited to subtle modification on the conventional detection network designed for natural images. However, there exists a vast domain gap between medical images and natural images where the medical image detection often suffers from several domain-specific challenges, such as high lesion/background similarity, dominant tiny lesions, and severe class imbalance. Is a hand-crafted detection network tailored for natural image undoubtedly good enough over a discrepant medical lesion domain? Is there more powerful operations, filters, and sub-networks that better fit the medical lesion detection problem to be discovered? In this paper, we introduce a novel ElixirNet that includes three components: 1) TruncatedRPN balances positive and negative data for false positive reduction; 2) Auto-lesion Block is automatically customized for medical images to incorporate relation-aware operations among region proposals, and leads to more suitable and efficient classification and localization. 3) Relation transfer module incorporates the semantic relationship and transfers the relevant contextual information with an interpretable the graph thus alleviates the problem of lack of annotations for all types of lesions. Experiments on DeepLesion and Kits19 prove the effectiveness of ElixirNet, achieving improvement of both sensitivity and precision over FPN with fewer parameters. | ['Xiaodan Liang', 'Shaoju Wang', 'Chenhan Jiang', 'Nong Xiao', 'Hang Xu'] | 2020-03-03 | null | null | null | null | ['medical-image-detection'] | ['computer-vision'] | [ 4.03246969e-01 3.26476008e-01 -6.29887223e-01 -2.36336350e-01
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b5bec304-7875-477e-9c09-cf0d4b416780 | permutation-invariant-variational-autoencoder | 2104.09856 | null | https://arxiv.org/abs/2104.09856v2 | https://arxiv.org/pdf/2104.09856v2.pdf | Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning | Recently, there has been great success in applying deep neural networks on graph structured data. Most work, however, focuses on either node- or graph-level supervised learning, such as node, link or graph classification or node-level unsupervised learning (e.g. node clustering). Despite its wide range of possible applications, graph-level unsupervised learning has not received much attention yet. This might be mainly attributed to the high representation complexity of graphs, which can be represented by n! equivalent adjacency matrices, where n is the number of nodes. In this work we address this issue by proposing a permutation-invariant variational autoencoder for graph structured data. Our proposed model indirectly learns to match the node ordering of input and output graph, without imposing a particular node ordering or performing expensive graph matching. We demonstrate the effectiveness of our proposed model on various graph reconstruction and generation tasks and evaluate the expressive power of extracted representations for downstream graph-level classification and regression. | ['Djork-Arné Clevert', 'Frank Noé', 'Robin Winter'] | 2021-04-20 | null | http://proceedings.neurips.cc/paper/2021/hash/4f3d7d38d24b740c95da2b03dc3a2333-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/4f3d7d38d24b740c95da2b03dc3a2333-Paper.pdf | neurips-2021-12 | ['graph-reconstruction'] | ['graphs'] | [ 3.72867286e-01 6.73023522e-01 -2.75333017e-01 -2.73572654e-01
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bd114b13-0656-4fe7-97ac-6cc686c35c99 | 190511559 | 1905.11559 | null | https://arxiv.org/abs/1905.11559v1 | https://arxiv.org/pdf/1905.11559v1.pdf | Road Segmentation with Image-LiDAR Data Fusion | Robust road segmentation is a key challenge in self-driving research. Though many image-based methods have been studied and high performances in dataset evaluations have been reported, developing robust and reliable road segmentation is still a major challenge. Data fusion across different sensors to improve the performance of road segmentation is widely considered an important and irreplaceable solution. In this paper, we propose a novel structure to fuse image and LiDAR point cloud in an end-to-end semantic segmentation network, in which the fusion is performed at decoder stage instead of at, more commonly, encoder stage. During fusion, we improve the multi-scale LiDAR map generation to increase the precision of the multi-scale LiDAR map by introducing pyramid projection method. Additionally, we adapted the multi-path refinement network with our fusion strategy and improve the road prediction compared with transpose convolution with skip layers. Our approach has been tested on KITTI ROAD dataset and has competitive performance. | ['Zeren Sun', 'Yazhou Yao', 'Xiangrui Li', 'Huafeng Liu', 'Zhenmin Tang', 'Ke Jia'] | 2019-05-26 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 4.01773632e-01 -1.46076411e-01 1.63378686e-01 -7.12180257e-01
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1c09524a-33cf-482b-a4ae-8f60270ec1c4 | deep-neural-network-for-semantic-based-text | 1908.01403 | null | https://arxiv.org/abs/1908.01403v3 | https://arxiv.org/pdf/1908.01403v3.pdf | Deep Neural Network for Semantic-based Text Recognition in Images | State-of-the-art text spotting systems typically aim to detect isolated words or word-by-word text in images of natural scenes and ignore the semantic coherence within a region of text. However, when interpreted together, seemingly isolated words may be easier to recognize. On this basis, we propose a novel "semantic-based text recognition" (STR) deep learning model that reads text in images with the help of understanding context. STR consists of several modules. We introduce the Text Grouping and Arranging (TGA) algorithm to connect and order isolated text regions. A text-recognition network interprets isolated words. Benefiting from semantic information, a sequenceto-sequence network model efficiently corrects inaccurate and uncertain phrases produced earlier in the STR pipeline. We present experiments on two new distinct datasets that contain scanned catalog images of interior designs and photographs of protesters with hand-written signs, respectively. Our results show that our STR model outperforms a baseline method that uses state-of-the-art single-wordrecognition techniques on both datasets. STR yields a high accuracy rate of 90% on the catalog images and 71% on the more difficult protest images, suggesting its generality in recognizing text. | ['Qitong Wang', 'Yi Zheng', 'Margrit Betke'] | 2019-08-04 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 5.90622962e-01 -1.91764995e-01 -3.35999243e-02 -5.68886876e-01
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1e62d0e5-1062-433c-9b82-1105e86fb9bd | teaching-small-language-models-to-reason | 2212.08410 | null | https://arxiv.org/abs/2212.08410v3 | https://arxiv.org/pdf/2212.08410v3.pdf | Teaching Small Language Models to Reason | Chain of thought prompting successfully improves the reasoning capabilities of large language models, achieving state of the art results on a range of datasets. However, these reasoning capabilities only appear to emerge in models with a size of over 100 billion parameters. In this paper, we explore the transfer of such reasoning capabilities to models with less than 100 billion parameters via knowledge distillation. Specifically, we finetune a student model on the chain of thought outputs generated by a larger teacher model. Our experiments show that the proposed method improves task performance across arithmetic, commonsense and symbolic reasoning datasets. For example, the accuracy of T5 XXL on GSM8K improves from 8.11% to 21.99% when finetuned on PaLM-540B generated chains of thought. | ['Aliaksei Severyn', 'Eric Malmi', 'Jakub Adamek', 'Jonathan Mallinson', 'Lucie Charlotte Magister'] | 2022-12-16 | null | null | null | null | ['gsm8k'] | ['natural-language-processing'] | [ 1.67403948e-02 6.21010244e-01 8.64755288e-02 -1.94209054e-01
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41e6eaac-9601-44cd-9521-63b76213d42b | progressive-multi-task-learning-framework-for | 2306.17447 | null | https://arxiv.org/abs/2306.17447v2 | https://arxiv.org/pdf/2306.17447v2.pdf | Progressive Multi-task Learning Framework for Chinese Text Error Correction | Chinese Text Error Correction (CTEC) aims to detect and correct errors in the input text, which benefits human's daily life and various downstream tasks. Recent approaches mainly employ Pre-trained Language Models (PLMs) to resolve CTEC task and achieve tremendous success. However, previous approaches suffer from issues of over-correction and under-correction, and the former is especially conspicuous in the precision-critical CTEC task. To mitigate the issue of overcorrection, we propose a novel model-agnostic progressive multitask learning framework for CTEC, named ProTEC, which guides a CTEC model to learn the task from easy to difficult. We divide CTEC task into three sub-tasks from easy to difficult: Error Detection, Error Type Identification, and Correction Result Generation. During the training process, ProTEC guides the model to learn text error correction progressively by incorporating these sub-tasks into a multi-task training objective. During the inference process, the model completes these sub-tasks in turn to generate the correction results. Extensive experiments and detailed analyses fully demonstrate the effectiveness and efficiency of our proposed framework. | ['Ying Shen', 'Hai-Tao Zheng', 'Yangning Li', 'Shulin Huang', 'Haojing Huang', 'Yinghui Li', 'Shirong Ma'] | 2023-06-30 | null | null | null | null | ['multi-task-learning'] | ['methodology'] | [ 3.89824450e-01 -3.62063676e-01 1.42296880e-01 -2.91233152e-01
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2.97273695e-01 -1.37876117e+00 1.34188563e-01 1.04477501e+00
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2.53211677e-01 1.06336689e+00 -1.23342901e-01 -5.33547819e-01
4.23834294e-01 1.64420366e-01 -4.44381498e-03 -7.72330344e-01
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3.87473732e-01 9.73556221e-01 4.17987257e-01 -1.53877631e-01
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5.18127561e-01 -3.46983165e-01 -1.30916700e-01 5.16842343e-02] | [10.988786697387695, 10.825719833374023] |
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